IT Exam Questions and Solutions Library
HOTSPOT - For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point. Hot Area:Suggested Answer:
Anomaly detection encompasses many important tasks in machine learning: Identifying transactions that are potentially fraudulent. Learning patterns that indicate that a network intrusion has occurred. Finding abnormal clusters of patients. Checking values entered into a system. Reference: https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/anomaly-detection
HOTSPOT - For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point. Hot Area:Suggested Answer:
Box 1: Yes - Azure Machine Learning designer lets you visually connect datasets and modules on an interactive canvas to create machine learning models. Box 2: Yes - With the designer you can connect the modules to create a pipeline draft. As you edit a pipeline in the designer, your progress is saved as a pipeline draft. Box 3: No - Reference: https://docs.microsoft.com/en-us/azure/machine-learning/concept-designer
HOTSPOT - Select the answer that correctly completes the sentence. Hot Area:Suggested Answer:
Fairness is a core ethical principle that all humans aim to understand and apply. This principle is even more important when AI systems are being developed. Key checks and balances need to make sure that the system's decisions don't discriminate or run a gender, race, sexual orientation, or religion bias toward a group or individual. Reference: https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/innovate/best-practices/trusted-ai
You have a dataset that contains information about taxi journeys that occurred during a given period. You need to train a model to predict the fare of a taxi journey. What should you use as a feature? A. the number of taxi journeys in the dataset B. the trip distance of individual taxi journeys C. the fare of individual taxi journeys D. the trip ID of individual taxi journeys Suggested Answer: B The label is the column you want to predict. The identified Featuresare the inputs you give the model to predict the Label. Example: The provided data set contains the following columns: vendor_id: The ID of the taxi vendor is a feature. rate_code: The rate type of the taxi trip is a feature. passenger_count: The number of passengers on the trip is a feature. trip_time_in_secs: The amount of time the trip took. You want to predict the fare of the trip before the trip is completed. At that moment, you don't know how long the trip would take. Thus, the trip time is not a feature and you'll exclude this column from the model. trip_distance: The distance of the trip is a feature. payment_type: The payment method (cash or credit card) is a feature. fare_amount: The total taxi fare paid is the label. Reference: https://docs.microsoft.com/en-us/dotnet/machine-learning/tutorials/predict-prices
You need to predict the sea level in meters for the next 10 years. Which type of machine learning should you use? A. classification B. regression C. clustering Suggested Answer: B In the most basic sense, regression refers to prediction of a numeric target. Linear regression attempts to establish a linear relationship between one or more independent variables and a numeric outcome, or dependent variable. You use this module to define a linear regression method, and then train a model using a labeled dataset. The trained model can then be used to make predictions. Reference: https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/linear-regression
HOTSPOT - Select the answer that correctly completes the sentence.Suggested Answer:
DRAG DROP - Match the principles of responsible AI to appropriate requirements. To answer, drag the appropriate principles from the column on the left to its requirement on the right. Each principle may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content. NOTE: Each correct selection is worth one point. Select and Place:Suggested Answer:
Reference: https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/innovate/best-practices/trusted-ai https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/4-guiding-principles
You run a charity event that involves posting photos of people wearing sunglasses on Twitter. You need to ensure that you only retweet photos that meet the following requirements: ✑ Include one or more faces. ✑ Contain at least one person wearing sunglasses. What should you use to analyze the images? A. the Verify operation in the Face service B. the Detect operation in the Face service C. the Describe Image operation in the Computer Vision service D. the Analyze Image operation in the Computer Vision service Suggested Answer: B Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/face/overview
HOTSPOT - For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point. Hot Area:Suggested Answer:
Box 1: Yes - Achieving transparency helps the team to understand the data and algorithms used to train the model, what transformation logic was applied to the data, the final model generated, and its associated assets. This information offers insights about how the model was created, which allows it to be reproduced in a transparent way. Box 2: No - A data holder is obligated to protect the data in an AI system, and privacy and security are an integral part of this system. Personal needs to be secured, and it should be accessed in a way that doesn't compromise an individual's privacy. Box 3: No - Inclusiveness mandates that AI should consider all human races and experiences, and inclusive design practices can help developers to understand and address potential barriers that could unintentionally exclude people. Where possible, speech-to-text, text-to-speech, and visual recognition technology should be used to empower people with hearing, visual, and other impairments. Reference: https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/innovate/best-practices/trusted-ai
HOTSPOT - To complete the sentence, select the appropriate option in the answer area. Hot Area:Suggested Answer:
Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/concept-object-detection
DRAG DROP - Match the Microsoft guiding principles for responsible AI to the appropriate descriptions. To answer, drag the appropriate principle from the column on the left to its description on the right. Each principle may be used once, more than once, or not at all. NOTE: Each correct selection is worth one point. Select and Place:Suggested Answer:
Box 1: Reliability and safety - To build trust, it's critical that AI systems operate reliably, safely, and consistently under normal circumstances and in unexpected conditions. These systems should be able to operate as they were originally designed, respond safely to unanticipated conditions, and resist harmful manipulation. Box 2: Accountability - The people who design and deploy AI systems must be accountable for how their systems operate. Organizations should draw upon industry standards to develop accountability norms. These norms can ensure that AI systems are not the final authority on any decision that impacts people's lives and that humans maintain meaningful control over otherwise highly autonomous AI systems. Box 3: Privacy and security - As AI becomes more prevalent, protecting privacy and securing important personal and business information is becoming more critical and complex. With AI, privacy and data security issues require especially close attention because access to data is essential for AI systems to make accurate and informed predictions and decisions about people. AI systems must comply with privacy laws that require transparency about the collection, use, and storage of data and mandate that consumers have appropriate controls to choose how their data is used Reference: https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/4-guiding-principles
You are designing an AI system that empowers everyone, including people who have hearing, visual, and other impairments. This is an example of which Microsoft guiding principle for responsible AI? A. fairness B. inclusiveness C. reliability and safety D. accountability Suggested Answer: B Inclusiveness: At Microsoft, we firmly believe everyone should benefit from intelligent technology, meaning it must incorporate and address a broad range of human needs and experiences. For the 1 billion people with disabilities around the world, AI technologies can be a game-changer. Reference: https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/4-guiding-principles
HOTSPOT - To complete the sentence, select the appropriate option in the answer area. Hot Area:Suggested Answer:
Reference: https://docs.microsoft.com/en-us/azure/machine-learning/team-data-science-process/create-features
DRAG DROP - Match the types of AI workloads to the appropriate scenarios. To answer, drag the appropriate workload type from the column on the left to its scenario on the right. Each workload type may be used once, more than once, or not at all. NOTE: Each correct selection is worth one point. Select and Place:Suggested Answer:
Box 3: Natural language processing Natural language processing (NLP) is used for tasks such as sentiment analysis, topic detection, language detection, key phrase extraction, and document categorization. Reference: https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing
HOTSPOT - To complete the sentence, select the appropriate option in the answer area. Hot Area:Suggested Answer:
Reliability and safety: AI systems need to be reliable and safe in order to be trusted. It is important for a system to perform as it was originally designed and for it to respond safely to new situations. Its inherent resilience should resist intended or unintended manipulation. Rigorous testing and validation should be established for operating conditions to ensure that the system responds safely to edge cases, and A/B testing and champion/challenger methods should be integrated into the evaluation process. An AI system's performance can degrade over time, so a robust monitoring and model tracking process needs to be established to reactively and proactively measure the model's performance and retrain it, as necessary, to modernize it. Reference: https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/innovate/best-practices/trusted-ai
You build a machine learning model by using the automated machine learning user interface (UI). You need to ensure that the model meets the Microsoft transparency principle for responsible AI. What should you do? A. Set Validation type to Auto. B. Enable Explain best model. C. Set Primary metric to accuracy. D. Set Max concurrent iterations to 0. Suggested Answer: B Model Explain Ability. Most businesses run on trust and being able to open the ML ג€black boxג€ helps build transparency and trust. In heavily regulated industries like healthcare and banking, it is critical to comply with regulations and best practices. One key aspect of this is understanding the relationship between input variables (features) and model output. Knowing both the magnitude and direction of the impact each feature (feature importance) has on the predicted value helps better understand and explain the model. With model explain ability, we enable you to understand feature importance as part of automated ML runs. Reference: https://azure.microsoft.com/en-us/blog/new-automated-machine-learning-capabilities-in-azure-machine-learning-service/
DRAG DROP - Match the types of AI workloads to the appropriate scenarios. To answer, drag the appropriate workload type from the column on the left to its scenario on the right. Each workload type may be used once, more than once, or not at all. NOTE: Each correct selection is worth one point. Select and Place:Suggested Answer:
Reference: https://docs.microsoft.com/en-us/learn/paths/get-started-with-artificial-intelligence-on-azure/
For a machine learning progress, how should you split data for training and evaluation? A. Use features for training and labels for evaluation. B. Randomly split the data into rows for training and rows for evaluation. C. Use labels for training and features for evaluation. D. Randomly split the data into columns for training and columns for evaluation. Suggested Answer: B The Split Data module is particularly useful when you need to separate data into training and testing sets. Use the Split Rows option if you want to divide the data into two parts. You can specify the percentage of data to put in each split, but by default, the data is divided 50-50. You can also randomize the selection of rows in each group, and use stratified sampling. Reference: https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/split-data
A company employs a team of customer service agents to provide telephone and email support to customers. The company develops a webchat bot to provide automated answers to common customer queries. Which business benefit should the company expect as a result of creating the webchat bot solution? A. increased sales B. a reduced workload for the customer service agents C. improved product reliability Suggested Answer: B
Which type of machine learning should you use to predict the number of gift cards that will be sold next month? A. classification B. regression C. clustering Suggested Answer: B In the most basic sense, regression refers to prediction of a numeric target. Linear regression attempts to establish a linear relationship between one or more independent variables and a numeric outcome, or dependent variable. You use this module to define a linear regression method, and then train a model using a labeled dataset. The trained model can then be used to make predictions. Reference: https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/linear-regression
HOTSPOT - To complete the sentence, select the appropriate option in the answer area. Hot Area:Suggested Answer:
Reliability and safety: To build trust, it's critical that AI systems operate reliably, safely, and consistently under normal circumstances and in unexpected conditions. These systems should be able to operate as they were originally designed, respond safely to unanticipated conditions, and resist harmful manipulation. Reference: https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/4-guiding-principles
DRAG DROP - You plan to deploy an Azure Machine Learning model as a service that will be used by client applications. Which three processes should you perform in sequence before you deploy the model? To answer, move the appropriate processes from the list of processes to the answer area and arrange them in the correct order. Select and Place:Suggested Answer:
Reference: https://docs.microsoft.com/en-us/azure/machine-learning/concept-ml-pipelines
You are building an AI system. Which task should you include to ensure that the service meets the Microsoft transparency principle for responsible AI? A. Ensure that all visuals have an associated text that can be read by a screen reader. B. Enable autoscaling to ensure that a service scales based on demand. C. Provide documentation to help developers debug code. D. Ensure that a training dataset is representative of the population. Suggested Answer: C Reference: https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/4-guiding-principles
What are three Microsoft guiding principles for responsible AI? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point. A. knowledgeability B. decisiveness C. inclusiveness D. fairness E. opinionatedness F. reliability and safety Suggested Answer: CDF Reference: https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/4-guiding-principles
Which service should you use to extract text, key/value pairs, and table data automatically from scanned documents? A. Form Recognizer B. Text Analytics C. Language Understanding D. Custom Vision Suggested Answer: A Accelerate your business processes by automating information extraction. Form Recognizer applies advanced machine learning to accurately extract text, key/ value pairs, and tables from documents. With just a few samples, Form Recognizer tailors its understanding to your documents, both on-premises and in the cloud. Turn forms into usable data at a fraction of the time and cost, so you can focus more time acting on the information rather than compiling it. Reference: https://azure.microsoft.com/en-us/services/cognitive-services/form-recognizer/
You use Azure Machine Learning designer to publish an inference pipeline. Which two parameters should you use to access the web service? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point. A. the model name B. the training endpoint C. the authentication key D. the REST endpoint Suggested Answer: CD You can consume a published pipeline in the Published pipelines page. Select a published pipeline and find the REST endpoint of it. To consume the pipeline, you need: ✑ The REST endpoint for your service ✑ The Primary Key for your service Reference: https://docs.microsoft.com/en-in/learn/modules/create-regression-model-azure-machine-learning-designer/deploy-service
HOTSPOT - For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point. Hot Area:Suggested Answer:
Box 1: Yes - In machine learning, if you have labeled data, that means your data is marked up, or annotated, to show the target, which is the answer you want your machine learning model to predict. In general, data labeling can refer to tasks that include data tagging, annotation, classification, moderation, transcription, or processing. Box 2: No - Box 3: No - Accuracy is simply the proportion of correctly classified instances. It is usually the first metric you look at when evaluating a classifier. However, when the test data is unbalanced (where most of the instances belong to one of the classes), or you are more interested in the performance on either one of the classes, accuracy doesn't really capture the effectiveness of a classifier. Reference: https://www.cloudfactory.com/data-labeling-guide https://docs.microsoft.com/en-us/azure/machine-learning/studio/evaluate-model-performance
HOTSPOT - To complete the sentence, select the appropriate option in the answer area. Hot Area:Suggested Answer:
Two-class classification provides the answer to simple two-choice questions such as Yes/No or True/False.
HOTSPOT - For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point. Hot Area:Suggested Answer:
Box 1: Yes - Automated machine learning, also referred to as automated ML or AutoML, is the process of automating the time consuming, iterative tasks of machine learning model development. It allows data scientists, analysts, and developers to build ML models with high scale, efficiency, and productivity all while sustaining model quality. Box 2: No - Box 3: Yes - During training, Azure Machine Learning creates a number of pipelines in parallel that try different algorithms and parameters for you. The service iterates through ML algorithms paired with feature selections, where each iteration produces a model with a training score. The higher the score, the better the model is considered to "fit" your data. It will stop once it hits the exit criteria defined in the experiment. Box 4: No - Apply automated ML when you want Azure Machine Learning to train and tune a model for you using the target metric you specify. The label is the column you want to predict. Reference: https://azure.microsoft.com/en-us/services/machine-learning/automatedml/#features
You have the Predicted vs. True chart shown in the following exhibit.Which type of model is the chart used to evaluate? A. classification B. regression C. clustering Suggested Answer: B What is a Predicted vs. True chart? Predicted vs. True shows the relationship between a predicted value and its correlating true value for a regression problem. This graph can be used to measure performance of a model as the closer to the y=x line the predicted values are, the better the accuracy of a predictive model. Reference: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-m
HOTSPOT - To complete the sentence, select the appropriate option in the answer area. Hot Area:Suggested Answer:
Reference: https://docs.microsoft.com/en-us/azure/machine-learning/concept-designer
DRAG DROP - Match the machine learning tasks to the appropriate scenarios. To answer, drag the appropriate task from the column on the left to its scenario on the right. Each task may be used once, more than once, or not at all. NOTE: Each correct selection is worth one point. Select and Place:Suggested Answer:
Box 1: Model evaluation - The Model evaluation module outputs a confusion matrix showing the number of true positives, false negatives, false positives, and true negatives, as well as ROC, Precision/Recall, and Lift curves. Box 2: Feature engineering - Feature engineering is the process of using domain knowledge of the data to create features that help ML algorithms learn better. In Azure Machine Learning, scaling and normalization techniques are applied to facilitate feature engineering. Collectively, these techniques and feature engineering are referred to as featurization. Note: Often, features are created from raw data through a process of feature engineering. For example, a time stamp in itself might not be useful for modeling until the information is transformed into units of days, months, or categories that are relevant to the problem, such as holiday versus working day. Box 3: Feature selection - In machine learning and statistics, feature selection is the process of selecting a subset of relevant, useful features to use in building an analytical model. Feature selection helps narrow the field of data to the most valuable inputs. Narrowing the field of data helps reduce noise and improve training performance. Reference: https://docs.microsoft.com/en-us/azure/machine-learning/studio/evaluate-model-performance https://docs.microsoft.com/en-us/azure/machine-learning/concept-automated-ml
HOTSPOT - To complete the sentence, select the appropriate option in the answer area. Hot Area:Suggested Answer:
Reference: https://www.baeldung.com/cs/feature-vs-label https://machinelearningmastery.com/discover-feature-engineering-how-to-engineer-features-and-how-to-get-good-at-it/
DRAG DROP - Match the types of AI workloads to the appropriate scenarios. To answer, drag the appropriate workload type from the column on the left to its scenario on the right. Each workload type may be used once, more than once, or not at all. NOTE: Each correct selection is worth one point. Select and Place:Suggested Answer:
Box 1: Knowledge mining - You can use Azure Cognitive Search's knowledge mining results and populate your knowledge base of your chatbot. Box 2: Computer vision - Box 3: Natural language processing Natural language processing (NLP) is used for tasks such as sentiment analysis. Reference: https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing
You are evaluating whether to use a basic workspace or an enterprise workspace in Azure Machine Learning. What are two tasks that require an enterprise workspace? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point. A. Use a graphical user interface (GUI) to run automated machine learning experiments. B. Create a compute instance to use as a workstation. C. Use a graphical user interface (GUI) to define and run machine learning experiments from Azure Machine Learning designer. D. Create a dataset from a comma-separated value (CSV) file. Suggested Answer: AC Note: Enterprise workspaces are no longer available as of September 2020. The basic workspace now has all the functionality of the enterprise workspace. Reference: https://www.azure.cn/en-us/pricing/details/machine-learning/ https://docs.microsoft.com/en-us/azure/machine-learning/concept-workspace
You are building an AI-based app. You need to ensure that the app uses the principles for responsible AI. Which two principles should you follow? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point. A. Implement an Agile software development methodology B. Implement a process of AI model validation as part of the software review process C. Establish a risk governance committee that includes members of the legal team, members of the risk management team, and a privacy officer D. Prevent the disclosure of the use of AI-based algorithms for automated decision making Suggested Answer: BC Reference: https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/innovate/best-practices/trusted-ai https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/3-implications-responsible-ai-practical
A medical research project uses a large anonymized dataset of brain scan images that are categorized into predefined brain haemorrhage types. You need to use machine learning to support early detection of the different brain haemorrhage types in the images before the images are reviewed by a person. This is an example of which type of machine learning? A. clustering B. regression C. classification Suggested Answer: C Reference: https://docs.microsoft.com/en-us/learn/modules/create-classification-model-azure-machine-learning-designer/introduction
HOTSPOT - To complete the sentence, select the appropriate option in the answer area. Hot Area:Suggested Answer:
Reference: https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/innovate/best-practices/trusted-ai
When training a model, why should you randomly split the rows into separate subsets? A. to train the model twice to attain better accuracy B. to train multiple models simultaneously to attain better performance C. to test the model by using data that was not used to train the model Suggested Answer: C
HOTSPOT - To complete the sentence, select the appropriate option in the answer area. Hot Area:Suggested Answer:
To perform real-time inferencing, you must deploy a pipeline as a real-time endpoint. Real-time endpoints must be deployed to an Azure Kubernetes Service cluster. Reference: https://docs.microsoft.com/en-us/azure/machine-learning/concept-designer#deploy
HOTSPOT - For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point. Hot Area:Suggested Answer:
Reference: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-designer-python https://docs.microsoft.com/en-us/azure/machine-learning/concept-automated-ml
When you design an AI system to assess whether loans should be approved, the factors used to make the decision should be explainable. This is an example of which Microsoft guiding principle for responsible AI? A. transparency B. inclusiveness C. fairness D. privacy and security Suggested Answer: A Achieving transparency helps the team to understand the data and algorithms used to train the model, what transformation logic was applied to the data, the final model generated, and its associated assets. This information offers insights about how the model was created, which allows it to be reproduced in a transparent way. Incorrect Answers: B: Inclusiveness mandates that AI should consider all human races and experiences, and inclusive design practices can help developers to understand and address potential barriers that could unintentionally exclude people. Where possible, speech-to-text, text-to-speech, and visual recognition technology should be used to empower people with hearing, visual, and other impairments. C: Fairness is a core ethical principle that all humans aim to understand and apply. This principle is even more important when AI systems are being developed. Key checks and balances need to make sure that the system's decisions don't discriminate or run a gender, race, sexual orientation, or religion bias toward a group or individual. D: A data holder is obligated to protect the data in an AI system, and privacy and security are an integral part of this system. Personal needs to be secured, and it should be accessed in a way that doesn't compromise an individual's privacy. Reference: https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/innovate/best-practices/trusted-ai https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/strategy/responsible-ai
HOTSPOT - You have the following dataset.You plan to use the dataset to train a model that will predict the house price categories of houses. What are Household Income and House Price Category? To answer, select the appropriate option in the answer area. NOTE: Each correct selection is worth one point. Hot Area:
Suggested Answer:
Reference: https://docs.microsoft.com/en-us/azure/machine-learning/studio/interpret-model-results
HOTSPOT - To complete the sentence, select the appropriate option in the answer area. Hot Area:Suggested Answer:
In the most basic sense, regression refers to prediction of a numeric target. Linear regression attempts to establish a linear relationship between one or more independent variables and a numeric outcome, or dependent variable. You use this module to define a linear regression method, and then train a model using a labeled dataset. The trained model can then be used to make predictions. Incorrect Answers: ✑ Classification is a machine learning method that uses data to determine the category, type, or class of an item or row of data. ✑ Clustering, in machine learning, is a method of grouping data points into similar clusters. It is also called segmentation. Over the years, many clustering algorithms have been developed. Almost all clustering algorithms use the features of individual items to find similar items. For example, you might apply clustering to find similar people by demographics. You might use clustering with text analysis to group sentences with similar topics or sentiment. Reference: https://docs.microsoft.com/en-us/azure/machine-learning/algorithm-module-reference/linear-regression https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/machine-learning-initialize-model-clustering
HOTSPOT - To complete the sentence, select the appropriate option in the answer area. Hot Area:Suggested Answer:
Accelerate your business processes by automating information extraction. Form Recognizer applies advanced machine learning to accurately extract text, key/ value pairs, and tables from documents. With just a few samples, Form Recognizer tailors its understanding to your documents, both on-premises and in the cloud. Turn forms into usable data at a fraction of the time and cost, so you can focus more time acting on the information rather than compiling it. Reference: https://azure.microsoft.com/en-us/services/cognitive-services/form-recognizer/
Which type of machine learning should you use to identify groups of people who have similar purchasing habits? A. classification B. regression C. clustering Suggested Answer: C Clustering is a machine learning task that is used to group instances of data into clusters that contain similar characteristics. Clustering can also be used to identify relationships in a dataset Reference: https://docs.microsoft.com/en-us/dotnet/machine-learning/resources/tasks
HOTSPOT - To complete the sentence, select the appropriate option in the answer area. Hot Area:Suggested Answer:
Regression is a machine learning task that is used to predict the value of the label from a set of related features. Reference: https://docs.microsoft.com/en-us/dotnet/machine-learning/resources/tasks
DRAG DROP - You need to use Azure Machine Learning designer to build a model that will predict automobile prices. Which type of modules should you use to complete the model? To answer, drag the appropriate modules to the correct locations. Each module may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content. NOTE: Each correct selection is worth one point. Select and Place:Suggested Answer:
Box 1: Select Columns in Dataset For Columns to be cleaned, choose the columns that contain the missing values you want to change. You can choose multiple columns, but you must use the same replacement method in all selected columns. Example:
Box 2: Split data - Splitting data is a common task in machine learning. You will split your data into two separate datasets. One dataset will train the model and the other will test how well the model performed. Box 3: Linear regression - Because you want to predict price, which is a number, you can use a regression algorithm. For this example, you use a linear regression model. Reference: alt="Reference Image" /> Box 2: Split data - Splitting data is a common task in machine learning. You will split your data into two separate datasets. One dataset will train the model and the other will test how well the model performed. Box 3: Linear regression - Because you want to predict price, which is a number, you can use a regression algorithm. For this example, you use a linear regression model. Reference: https://docs.microsoft.com/en-us/azure/machine-learning/tutorial-designer-automobile-price-train-score
HOTSPOT - To complete the sentence, select the appropriate option in the answer area. Hot Area:Suggested Answer:
Regression is a machine learning task that is used to predict the value of the label from a set of related features. Reference: https://docs.microsoft.com/en-us/dotnet/machine-learning/resources/tasks
What are two metrics that you can use to evaluate a regression model? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point. A. coefficient of determination (R2) B. F1 score C. root mean squared error (RMSE) D. area under curve (AUC) E. balanced accuracy Suggested Answer: AC A: R-squared (R2), or Coefficient of determination represents the predictive power of the model as a value between -inf and 1.00. 1.00 means there is a perfect fit, and the fit can be arbitrarily poor so the scores can be negative. C: RMS-loss or Root Mean Squared Error (RMSE) (also called Root Mean Square Deviation, RMSD), measures the difference between values predicted by a model and the values observed from the environment that is being modeled. Incorrect Answers: B: F1 score also known as balanced F-score or F-measure is used to evaluate a classification model. D: aucROC or area under the curve (AUC) is used to evaluate a classification model. Reference: https://docs.microsoft.com/en-us/dotnet/machine-learning/resources/metrics
Which two actions are performed during the data ingestion and data preparation stage of an Azure Machine Learning process? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point. A. Calculate the accuracy of the model. B. Score test data by using the model. C. Combine multiple datasets. D. Use the model for real-time predictions. E. Remove records that have missing values. Suggested Answer: CE Reference: https://docs.microsoft.com/en-us/azure/machine-learning/concept-data-ingestion https://docs.microsoft.com/en-us/azure/architecture/data-science-process/prepare-data
HOTSPOT - For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point. Hot Area:Suggested Answer:
Box 1: No - The validation dataset is different from the test dataset that is held back from the training of the model. Box 2: Yes - A validation dataset is a sample of data that is used to give an estimate of model skill while tuning model's hyperparameters. Box 3: No - The Test Dataset, not the validation set, used for this. The Test Dataset is a sample of data used to provide an unbiased evaluation of a final model fit on the training dataset. Reference: https://machinelearningmastery.com/difference-test-validation-datasets/
HOTSPOT - For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point. Hot Area:Suggested Answer:
Reference: https://docs.microsoft.com/en-us/learn/modules/create-regression-model-azure-machine-learning-designer/5-create-training-pipeline https://docs.microsoft.com/en-us/learn/modules/create-classification-model-azure-machine-learning-designer/introduction https://docs.microsoft.com/en-us/learn/modules/create-clustering-model-azure-machine-learning-designer/1-introduction
You are building a tool that will process images from retail stores and identify the products of competitors. The solution will use a custom model. Which Azure Cognitive Services service should you use? A. Custom Vision B. Form Recognizer C. Face D. Computer Vision Suggested Answer: A Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/custom-vision-service/overview
HOTSPOT - You have an Azure Machine Learning model that predicts product quality. The model has a training dataset that contains 50,000 records. A sample of the data is shown in the following table.For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point. Hot Area:
Suggested Answer:
Reference: https://docs.microsoft.com/en-us/azure/machine-learning/component-reference/filter-based-feature-selection
HOTSPOT - For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point. Hot Area:Suggested Answer:
Clustering is a machine learning task that is used to group instances of data into clusters that contain similar characteristics. Clustering can also be used to identify relationships in a dataset Regression is a machine learning task that is used to predict the value of the label from a set of related features. Reference: https://docs.microsoft.com/en-us/dotnet/machine-learning/resources/tasks
HOTSPOT - To complete the sentence, select the appropriate option in the answer area. Hot Area:Suggested Answer:
Reference: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-label-data
You need to predict the income range of a given customer by using the following dataset.Which two fields should you use as features? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point. A. Education Level B. Last Name C. Age D. Income Range E. First Name Suggested Answer: AC First Name, Last Name, Age and Education Level are features. Income range is a label (what you want to predict). First Name and Last Name are irrelevant in that they have no bearing on income. Age and Education level are the features you should use.
HOTSPOT - To complete the sentence, select the appropriate option in the answer area. Hot Area:Suggested Answer:
Reference: https://docs.microsoft.com/en-us/azure/architecture/data-science-process/create-features
HOTSPOT - To complete the sentence, select the appropriate option in the answer area. Hot Area:Suggested Answer:
Reference: https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/innovate/best-practices/trusted-ai
HOTSPOT - To complete the sentence, select the appropriate option in the answer area. Hot Area:Suggested Answer:
Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/custom-vision-service/getting-started-build-a-classifier
DRAG DROP - Match the types of machine learning to the appropriate scenarios. To answer, drag the appropriate machine learning type from the column on the left to its scenario on the right. Each machine learning type may be used once, more than once, or not at all. NOTE: Each correct selection is worth one point. Select and Place:Suggested Answer:
Box 1: Regression - In the most basic sense, regression refers to prediction of a numeric target. Linear regression attempts to establish a linear relationship between one or more independent variables and a numeric outcome, or dependent variable. You use this module to define a linear regression method, and then train a model using a labeled dataset. The trained model can then be used to make predictions. Box 2: Clustering - Clustering, in machine learning, is a method of grouping data points into similar clusters. It is also called segmentation. Over the years, many clustering algorithms have been developed. Almost all clustering algorithms use the features of individual items to find similar items. For example, you might apply clustering to find similar people by demographics. You might use clustering with text analysis to group sentences with similar topics or sentiment. Box 3: Classification - Two-class classification provides the answer to simple two-choice questions such as Yes/No or True/False. Reference: https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/linear-regression
You need to create a training dataset and validation dataset from an existing dataset. Which module in the Azure Machine Learning designer should you use? A. Select Columns in Dataset B. Add Rows C. Split Data D. Join Data Suggested Answer: C A common way of evaluating a model is to divide the data into a training and test set by using Split Data, and then validate the model on the training data. Use the Split Data module to divide a dataset into two distinct sets. The studio currently supports training/validation data splits Reference: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-configure-cross-validation-data-splits
Which two components can you drag onto a canvas in Azure Machine Learning designer? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point. A. dataset B. compute C. pipeline D. module Suggested Answer: AD You can drag-and-drop datasets and modules onto the canvas. Reference: https://docs.microsoft.com/en-us/azure/machine-learning/concept-designer
Which metric can you use to evaluate a classification model? A. true positive rate B. mean absolute error (MAE) C. coefficient of determination (R2) D. root mean squared error (RMSE) Suggested Answer: A What does a good model look like? An ROC curve that approaches the top left corner with 100% true positive rate and 0% false positive rate will be the best model. A random model would display as a flat line from the bottom left to the top right corner. Worse than random would dip below the y=x line. Reference: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-ml#classification
HOTSPOT - For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point. Hot Area:Suggested Answer:
Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/custom-vision-service/get-started-build-detector
What is a use case for classification? A. predicting how many cups of coffee a person will drink based on how many hours the person slept the previous night. B. analyzing the contents of images and grouping images that have similar colors C. predicting whether someone uses a bicycle to travel to work based on the distance from home to work D. predicting how many minutes it will take someone to run a race based on past race times Suggested Answer: C Two-class classification provides the answer to simple two-choice questions such as Yes/No or True/False. Incorrect Answers: A: This is Regression. B: This is Clustering. D: This is Regression. Reference: https://docs.microsoft.com/en-us/azure/machine-learning/algorithm-module-reference/linear-regression https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/machine-learning-initialize-model-clustering
Your company wants to build a recycling machine for bottles. The recycling machine must automatically identify bottles of the correct shape and reject all other items. Which type of AI workload should the company use? A. anomaly detection B. conversational AI C. computer vision D. natural language processing Suggested Answer: C Azure's Computer Vision service gives you access to advanced algorithms that process images and return information based on the visual features you're interested in. For example, Computer Vision can determine whether an image contains adult content, find specific brands or objects, or find human faces. Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/overview
What are two tasks that can be performed by using the Computer Vision service? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point. A. Train a custom image classification model. B. Detect faces in an image. C. Recognize handwritten text. D. Translate the text in an image between languages. Suggested Answer: BC B: Azure's Computer Vision service provides developers with access to advanced algorithms that process images and return information based on the visual features you're interested in. For example, Computer Vision can determine whether an image contains adult content, find specific brands or objects, or find human faces. C: Computer Vision includes Optical Character Recognition (OCR) capabilities. You can use the new Read API to extract printed and handwritten text from images and documents. Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/home
Which two languages can you use to write custom code for Azure Machine Learning designer? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point. A. Python B. R C. C# D. Scala Suggested Answer: AB Use Azure Machine Learning designer for customizing using Python and R code. Reference: https://azure.microsoft.com/en-us/services/machine-learning/designer/#features
You send an image to a Computer Vision API and receive back the annotated image shown in the exhibit.Which type of computer vision was used? A. object detection B. face detection C. optical character recognition (OCR) D. image classification Suggested Answer: A Object detection is similar to tagging, but the API returns the bounding box coordinates (in pixels) for each object found. For example, if an image contains a dog, cat and person, the Detect operation will list those objects together with their coordinates in the image. You can use this functionality to process the relationships between the objects in an image. It also lets you determine whether there are multiple instances of the same tag in an image. The Detect API applies tags based on the objects or living things identified in the image. There is currently no formal relationship between the tagging taxonomy and the object detection taxonomy. At a conceptual level, the Detect API only finds objects and living things, while the Tag API can also include contextual terms like "indoor", which can't be localized with bounding boxes. Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/concept-object-detection
HOTSPOT - For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point. Hot Area:Suggested Answer:
Box 1: Yes - For regression problems, the label column must contain numeric data that represents the response variable. Ideally the numeric data represents a continuous scale. Box 2: No - K-Means Clustering - Because the K-means algorithm is an unsupervised learning method, a label column is optional. If your data includes a label, you can use the label values to guide selection of the clusters and optimize the model. If your data has no label, the algorithm creates clusters representing possible categories, based solely on the data. Box 3: No - For classification problems, the label column must contain either categorical values or discrete values. Some examples might be a yes/no rating, a disease classification code or name, or an income group. If you pick a noncategorical column, the component will return an error during training. Reference: https://docs.microsoft.com/en-us/azure/machine-learning/component-reference/train-model https://docs.microsoft.com/en-us/azure/machine-learning/component-reference/k-means-clustering
HOTSPOT - To complete the sentence, select the appropriate option in the answer area. Hot Area:Suggested Answer:
Azure Custom Vision is a cognitive service that lets you build, deploy, and improve your own image classifiers. An image classifier is an AI service that applies labels (which represent classes) to images, according to their visual characteristics. Unlike the Computer Vision service, Custom Vision allows you to specify the labels to apply. Note: The Custom Vision service uses a machine learning algorithm to apply labels to images. You, the developer, must submit groups of images that feature and lack the characteristics in question. You label the images yourself at the time of submission. Then the algorithm trains to this data and calculates its own accuracy by testing itself on those same images. Once the algorithm is trained, you can test, retrain, and eventually use it to classify new images according to the needs of your app. You can also export the model itself for offline use. Incorrect Answers: Computer Vision: Azure's Computer Vision service provides developers with access to advanced algorithms that process images and return information based on the visual features you're interested in. For example, Computer Vision can determine whether an image contains adult content, find specific brands or objects, or find human faces. Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/custom-vision-service/home
You need to predict the animal population of an area. Which Azure Machine Learning type should you use? A. regression B. clustering C. classification Suggested Answer: A Regression is a supervised machine learning technique used to predict numeric values. Reference: https://docs.microsoft.com/en-us/learn/modules/create-regression-model-azure-machine-learning-designer/1-introduction
You need to determine the location of cars in an image so that you can estimate the distance between the cars. Which type of computer vision should you use? A. optical character recognition (OCR) B. object detection C. image classification D. face detection Suggested Answer: B Object detection is similar to tagging, but the API returns the bounding box coordinates (in pixels) for each object found. For example, if an image contains a dog, cat and person, the Detect operation will list those objects together with their coordinates in the image. You can use this functionality to process the relationships between the objects in an image. It also lets you determine whether there are multiple instances of the same tag in an image. The Detect API applies tags based on the objects or living things identified in the image. There is currently no formal relationship between the tagging taxonomy and the object detection taxonomy. At a conceptual level, the Detect API only finds objects and living things, while the Tag API can also include contextual terms like "indoor", which can't be localized with bounding boxes. Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/concept-object-detection
DRAG DROP - Match the facial recognition tasks to the appropriate questions. To answer, drag the appropriate task from the column on the left to its question on the right. Each task may be used once, more than once, or not at all. NOTE: Each correct selection is worth one point. Select and Place:Suggested Answer:
Box 1: verification - Face verification: Check the likelihood that two faces belong to the same person and receive a confidence score. Box 2: similarity - Box 3: Grouping - Box 4: identification - Face detection: Detect one or more human faces along with attributes such as: age, emotion, pose, smile, and facial hair, including 27 landmarks for each face in the image. Reference: https://azure.microsoft.com/en-us/services/cognitive-services/face/#features
DRAG DROP - Match the types of machine learning to the appropriate scenarios. To answer, drag the appropriate machine learning type from the column on the left to its scenario on the right. Each machine learning type may be used once, more than once, or not at all. NOTE: Each correct selection is worth one point. Select and Place:Suggested Answer:
Box 1: Image classification - Image classification is a supervised learning problem: define a set of target classes (objects to identify in images), and train a model to recognize them using labeled example photos. Box 2: Object detection - Object detection is a computer vision problem. While closely related to image classification, object detection performs image classification at a more granular scale. Object detection both locates and categorizes entities within images. Box 3: Semantic Segmentation - Semantic segmentation achieves fine-grained inference by making dense predictions inferring labels for every pixel, so that each pixel is labeled with the class of its enclosing object ore region. Reference: https://developers.google.com/machine-learning/practica/image-classification https://docs.microsoft.com/en-us/dotnet/machine-learning/tutorials/object-detection-model-builder https://nanonets.com/blog/how-to-do-semantic-segmentation-using-deep-learning/
DRAG DROP - Match the types of computer vision workloads to the appropriate scenarios. To answer, drag the appropriate workload type from the column on the left to its scenario on the right. Each workload type may be used once, more than once, or not at all. NOTE: Each correct selection is worth one point. Select and Place:Suggested Answer:
Box 1: Facial recognition - Face detection that perceives faces and attributes in an image; person identification that matches an individual in your private repository of up to 1 million people; perceived emotion recognition that detects a range of facial expressions like happiness, contempt, neutrality, and fear; and recognition and grouping of similar faces in images. Box 2: OCR - Box 3: Objection detection - Object detection is similar to tagging, but the API returns the bounding box coordinates (in pixels) for each object found. For example, if an image contains a dog, cat and person, the Detect operation will list those objects together with their coordinates in the image. You can use this functionality to process the relationships between the objects in an image. It also lets you determine whether there are multiple instances of the same tag in an image. The Detect API applies tags based on the objects or living things identified in the image. There is currently no formal relationship between the tagging taxonomy and the object detection taxonomy. At a conceptual level, the Detect API only finds objects and living things, while the Tag API can also include contextual terms like "indoor", which can't be localized with bounding boxes. Reference: https://azure.microsoft.com/en-us/services/cognitive-services/face/ https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/concept-object-detection
You use drones to identify where weeds grow between rows of crops to send an instruction for the removal of the weeds. This is an example of which type of computer vision? A. object detection B. optical character recognition (OCR) C. scene segmentation Suggested Answer: A Object detection is similar to tagging, but the API returns the bounding box coordinates for each tag applied. For example, if an image contains a dog, cat and person, the Detect operation will list those objects together with their coordinates in the image. Incorrect Answers: B: Optical character recognition (OCR) allows you to extract printed or handwritten text from images and documents. C: Scene segmentation determines when a scene changes in video based on visual cues. A scene depicts a single event and it's composed by a series of consecutive shots, which are semantically related. Reference: https://docs.microsoft.com/en-us/ai-builder/object-detection-overview https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/overview-ocr https://docs.microsoft.com/en-us/azure/azure-video-analyzer/video-analyzer-for-media-docs/video-indexer-overview
HOTSPOT - For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point. Hot Area:Suggested Answer:
Box 1: Yes - Custom Vision functionality can be divided into two features. Image classification applies one or more labels to an image. Object detection is similar, but it also returns the coordinates in the image where the applied label(s) can be found. Box 2: Yes - The Custom Vision service uses a machine learning algorithm to analyze images. You, the developer, submit groups of images that feature and lack the characteristics in question. You label the images yourself at the time of submission. Then, the algorithm trains to this data and calculates its own accuracy by testing itself on those same images. Box 3: No - Custom Vision service can be used only on graphic files. Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/Custom-Vision-Service/overview
You are processing photos of runners in a race. You need to read the numbers on the runners' shirts to identity the runners in the photos. Which type of computer vision should you use? A. facial recognition B. optical character recognition (OCR) C. image classification D. object detection Suggested Answer: B Optical character recognition (OCR) allows you to extract printed or handwritten text from images and documents. Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/overview-ocr
You need to develop a mobile app for employees to scan and store their expenses while travelling. Which type of computer vision should you use? A. semantic segmentation B. image classification C. object detection D. optical character recognition (OCR) Suggested Answer: D Azure's Computer Vision API includes Optical Character Recognition (OCR) capabilities that extract printed or handwritten text from images. You can extract text from images, such as photos of license plates or containers with serial numbers, as well as from documents - invoices, bills, financial reports, articles, and more. Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/concept-recognizing-text
HOTSPOT - You have a database that contains a list of employees and their photos. You are tagging new photos of the employees. For each of the following statements select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point. Hot Area:Suggested Answer:
Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/face/overview https://docs.microsoft.com/en-us/azure/cognitive-services/face/concepts/face-detection
HOTSPOT - Select the answer that correctly completes the sentence. Hot Area:Suggested Answer:
Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/overview https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/intro-to-spatial-analysis-public-preview
In which two scenarios can you use the Form Recognizer service? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point. A. Extract the invoice number from an invoice. B. Translate a form from French to English. C. Find image of product in a catalog. D. Identify the retailer from a receipt. Suggested Answer: AD Reference: https://azure.microsoft.com/en-gb/services/cognitive-services/form-recognizer/#features
DRAG DROP - You need to scan the news for articles about your customers and alert employees when there is a negative article. Positive articles must be added to a press book. Which natural language processing tasks should you use to complete the process? To answer, drag the appropriate tasks to the correct locations. Each task may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content. NOTE: Each correct selection is worth one point. Select and Place:Suggested Answer:
Box 1: Entity recognition - the Named Entity Recognition module in Machine Learning Studio (classic), to identify the names of things, such as people, companies, or locations in a column of text. Named entity recognition is an important area of research in machine learning and natural language processing (NLP), because it can be used to answer many real-world questions, such as: ✑ Which companies were mentioned in a news article? ✑ Does a tweet contain the name of a person? Does the tweet also provide his current location? ✑ Were specified products mentioned in complaints or reviews? Box 2: Sentiment Analysis - The Text Analytics API's Sentiment Analysis feature provides two ways for detecting positive and negative sentiment. If you send a Sentiment Analysis request, the API will return sentiment labels (such as "negative", "neutral" and "positive") and confidence scores at the sentence and document-level. Reference: https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/named-entity-recognition https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/how-tos/text-analytics-how-to-sentiment-analysis
What are two tasks that can be performed by using computer vision? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point. A. Predict stock prices. B. Detect brands in an image. C. Detect the color scheme in an image D. Translate text between languages. E. Extract key phrases. Suggested Answer: BC B: Identify commercial brands in images or videos from a database of thousands of global logos. You can use this feature, for example, to discover which brands are most popular on social media or most prevalent in media product placement. C: Analyze color usage within an image. Computer Vision can determine whether an image is black & white or color and, for color images, identify the dominant and accent colors. Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/overview
HOTSPOT - For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point. Hot Area:Suggested Answer:
The translator service provides multi-language support for text translation, transliteration, language detection, and dictionaries. Speech-to-Text, also known as automatic speech recognition (ASR), is a feature of Speech Services that provides transcription. Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/Translator/translator-info-overview https://docs.microsoft.com/en-us/legal/cognitive-services/speech-service/speech-to-text/transparency-note
In which two scenarios can you use a speech synthesis solution? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point. A. an automated voice that reads back a credit card number entered into a telephone by using a numeric keypad B. generating live captions for a news broadcast C. extracting key phrases from the audio recording of a meeting D. an AI character in a computer game that speaks audibly to a player Suggested Answer: AD Azure Text to Speech is a Speech service feature that converts text to lifelike speech. Incorrect Answers: C: Extracting key phrases is not speech synthesis. Reference: https://azure.microsoft.com/en-in/services/cognitive-services/text-to-speech/
You plan to develop a bot that will enable users to query a knowledge base by using natural language processing. Which two services should you include in the solution? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point. A. QnA Maker B. Azure Bot Service C. Form Recognizer D. Anomaly Detector Suggested Answer: AB Reference: https://docs.microsoft.com/en-us/azure/bot-service/bot-service-overview-introduction?view=azure-bot-service-4.0 https://docs.microsoft.com/en-us/azure/cognitive-services/luis/choose-natural-language-processing-service
Your website has a chatbot to assist customers. You need to detect when a customer is upset based on what the customer types in the chatbot. Which type of AI workload should you use? A. anomaly detection B. computer vision C. regression D. natural language processing Suggested Answer: D Natural language processing (NLP) is used for tasks such as sentiment analysis, topic detection, language detection, key phrase extraction, and document categorization. Sentiment Analysis is the process of determining whether a piece of writing is positive, negative or neutral. Reference: https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing
HOTSPOT - For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point. Hot Area:Suggested Answer:
Reference: https://docs.microsoft.com/en-gb/azure/cognitive-services/text-analytics/overview https://azure.microsoft.com/en-gb/services/cognitive-services/speech-services/
HOTSPOT - To complete the sentence, select the appropriate option in the answer area. Hot Area:Suggested Answer:
Reference: https://azure.microsoft.com/en-gb/services/cognitive-services/speech-to-text/#features
In which two scenarios can you use speech recognition? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point. A. an in-car system that reads text messages aloud B. providing closed captions for recorded or live videos C. creating an automated public address system for a train station D. creating a transcript of a telephone call or meeting Suggested Answer: BD Reference: https://azure.microsoft.com/en-gb/services/cognitive-services/speech-to-text/#features
You need to build an app that will read recipe instructions aloud to support users who have reduced vision. Which version service should you use? A. Text Analytics B. Translator C. Speech D. Language Understanding (LUIS) Suggested Answer: C Reference: https://azure.microsoft.com/en-us/services/cognitive-services/text-to-speech/#features
Which service should you use to extract text, key/value pairs, and table data automatically from scanned documents? A. Custom Vision B. Face C. Form Recognizer D. Language Suggested Answer: C Form Recognizer applies advanced machine learning to accurately extract text, key-value pairs, tables, and structures from documents. Reference: https://azure.microsoft.com/en-us/services/form-recognizer/
HOTSPOT - Select the answer that correctly completes the sentence. Hot Area:Suggested Answer:
Handwriting OCR (optical character recognition) is the process of automatically extracting handwritten information from paper, scans and other low-quality digital documents. Reference: https://vidado.ai/handwriting-ocr
You are developing a solution that uses the Text Analytics service. You need to identify the main talking points in a collection of documents. Which type of natural language processing should you use? A. entity recognition B. key phrase extraction C. sentiment analysis D. language detection Suggested Answer: B Broad entity extraction: Identify important concepts in text, including key Key phrase extraction/ Broad entity extraction: Identify important concepts in text, including key phrases and named entities such as people, places, and organizations. Reference: https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing
Which Computer Vision feature can you use to generate automatic captions for digital photographs? A. Recognize text. B. Identify the areas of interest. C. Detect objects. D. Describe the images. Suggested Answer: D Describe images with human-readable language Computer Vision can analyze an image and generate a human-readable phrase that describes its contents. The algorithm returns several descriptions based on different visual features, and each description is given a confidence score. The final output is a list of descriptions ordered from highest to lowest confidence. The image description feature is part of the Analyze Image API. Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/concept-describing-images
HOTSPOT - To complete the sentence, select the appropriate option in the answer area. Hot Area:Suggested Answer:
Natural language processing (NLP) is used for tasks such as sentiment analysis, topic detection, language detection, key phrase extraction, and document categorization. Reference: https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing
DRAG DROP - Match the facial recognition tasks to the appropriate questions. To answer, drag the appropriate task from the column on the left to its question on the right. Each task may be used once, more than once, or not at all. NOTE: Each correct selection is worth one point. Select and Place:Suggested Answer:
Box 1: verification - Identity verification - Modern enterprises and apps can use the Face identification and Face verification operations to verify that a user is who they claim to be. Box 2: similarity - The Find Similar operation does face matching between a target face and a set of candidate faces, finding a smaller set of faces that look similar to the target face. This is useful for doing a face search by image. The service supports two working modes, matchPerson and matchFace. The matchPerson mode returns similar faces after filtering for the same person by using the Verify API. The matchFace mode ignores the same-person filter. It returns a list of similar candidate faces that may or may not belong to the same person. Box 3: identification - Face identification can address "one-to-many" matching of one face in an image to a set of faces in a secure repository. Match candidates are returned based on how closely their face data matches the query face. This scenario is used in granting building or airport access to a certain group of people or verifying the user of a device. Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/face/overview
In which scenario should you use key phrase extraction? A. identifying whether reviews of a restaurant are positive or negative B. generating captions for a video based on the audio track C. identifying which documents provide information about the same topics D. translating a set of documents from English to German Suggested Answer: C
In which two scenarios can you use the Form Recognizer service? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point. A. Identify the retailer from a receipt B. Translate from French to English C. Extract the invoice number from an invoice D. Find images of products in a catalog Suggested Answer: AC Reference: https://docs.microsoft.com/en-us/azure/applied-ai-services/form-recognizer/overview?tabs=v2-1
You are building a knowledge base by using QnA Maker. Which file format can you use to populate the knowledge base? A. PPTX B. XML C. ZIP D. PDF Suggested Answer: D D: Content types of documents you can add to a knowledge base: Content types include many standard structured documents such as PDF, DOC, and TXT. Note: The tool supports the following file formats for ingestion: ✑ .tsv: QnA contained in the format Question(tab)Answer. ✑ .txt, .docx, .pdf: QnA contained as regular FAQ content--that is, a sequence of questions and answers. Incorrect Answers: A: PPTX is the default presentation file format for new PowerPoint presentations. B: It is not possible to ingest xml file directly. Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/qnamaker/concepts/data-sources-and-content
You need to build an image tagging solution for social media that tags images of your friends automatically. Which Azure Cognitive Services service should you use? A. Face B. Form Recognizer C. Text Analytics D. Computer Vision Suggested Answer: A Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/face/overview https://docs.microsoft.com/en-us/azure/cognitive-services/face/face-api-how-to-topics/howtodetectfacesinimage
You build a QnA Maker bot by using a frequently asked questions (FAQ) page. You need to add professional greetings and other responses to make the bot more user friendly. What should you do? A. Increase the confidence threshold of responses B. Enable active learning C. Create multi-turn questions D. Add chit-chat Suggested Answer: D Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/qnamaker/how-to/chit-chat-knowledge-base?tabs=v1
You need to develop a chatbot for a website. The chatbot must answer users' questions based on the information in the following documents: ✑ A product troubleshooting guide in a Microsoft Word document ✑ A frequently asked questions (FAQ) list on a webpage Which service should you use to process the documents? A. Azure Bot Service B. Language Understanding C. Text Analytics D. QnA Maker Suggested Answer: D Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/QnAMaker/Overview/overview
DRAG DROP - You plan to apply Text Analytics API features to a technical support ticketing system. Match the Text Analytics API features to the appropriate natural language processing scenarios. To answer, drag the appropriate feature from the column on the left to its scenario on the right. Each feature may be used once, more than once, or not at all. NOTE: Each correct selection is worth one point. Select and Place:Suggested Answer:
Box1: Sentiment analysis - Sentiment Analysis is the process of determining whether a piece of writing is positive, negative or neutral. Box 2: Broad entity extraction - Broad entity extraction: Identify important concepts in text, including key Key phrase extraction/ Broad entity extraction: Identify important concepts in text, including key phrases and named entities such as people, places, and organizations. Box 3: Entity Recognition - Named Entity Recognition: Identify and categorize entities in your text as people, places, organizations, date/time, quantities, percentages, currencies, and more. Well-known entities are also recognized and linked to more information on the web. Reference: https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing https://azure.microsoft.com/en-us/services/cognitive-services/text-analytics
You are authoring a Language Understanding (LUIS) application to support a music festival. You want users to be able to ask questions about scheduled shows, such as: `Which act is playing on the main stage?` The question `Which act is playing on the main stage?` is an example of which type of element? A. an intent B. an utterance C. a domain D. an entity Suggested Answer: B Utterances are input from the user that your app needs to interpret. Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/LUIS/luis-concept-utterance
You are developing a natural language processing solution in Azure. The solution will analyze customer reviews and determine how positive or negative each review is. This is an example of which type of natural language processing workload? A. language detection B. sentiment analysis C. key phrase extraction D. entity recognition Suggested Answer: B Sentiment Analysis is the process of determining whether a piece of writing is positive, negative or neutral. Reference: https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing
You use natural language processing to process text from a Microsoft news story. You receive the output shown in the following exhibit.Which type of natural languages processing was performed? A. entity recognition B. key phrase extraction C. sentiment analysis D. translation Suggested Answer: A Named Entity Recognition (NER) is the ability to identify different entities in text and categorize them into pre-defined classes or types such as: person, location, event, product, and organization. In this question, the square brackets indicate the entities such as DateTime, PersonType, Skill. Reference: https://docs.microsoft.com/en-in/azure/cognitive-services/text-analytics/how-tos/text-analytics-how-to-entity-linking?tabs=version-3-preview
HOTSPOT - For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point. Hot Area:Suggested Answer:
The Text Analytics API is a cloud-based service that provides advanced natural language processing over raw text, and includes four main functions: sentiment analysis, key phrase extraction, named entity recognition, and language detection. Box 1: Yes - You can detect which language the input text is written in and report a single language code for every document submitted on the request in a wide range of languages, variants, dialects, and some regional/cultural languages. The language code is paired with a score indicating the strength of the score. Box 2: No - Box 3: Yes - Named Entity Recognition: Identify and categorize entities in your text as people, places, organizations, date/time, quantities, percentages, currencies, and more. Well-known entities are also recognized and linked to more information on the web. Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/overview
HOTSPOT - For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point. Hot Area:Suggested Answer:
Box 1: Yes - Content Moderator is part of Microsoft Cognitive Services allowing businesses to use machine assisted moderation of text, images, and videos that augment human review. The text moderation capability now includes a new machine-learning based text classification feature which uses a trained model to identify possible abusive, derogatory or discriminatory language such as slang, abbreviated words, offensive, and intentionally misspelled words for review. Box 2: No - Azure's Computer Vision service gives you access to advanced algorithms that process images and return information based on the visual features you're interested in. For example, Computer Vision can determine whether an image contains adult content, find specific brands or objects, or find human faces. Box 3: Yes - Natural language processing (NLP) is used for tasks such as sentiment analysis, topic detection, language detection, key phrase extraction, and document categorization. Sentiment Analysis is the process of determining whether a piece of writing is positive, negative or neutral. Reference: https://azure.microsoft.com/es-es/blog/machine-assisted-text-classification-on-content-moderator-public-preview/ https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing
DRAG DROP - Match the types of natural languages processing workloads to the appropriate scenarios. To answer, drag the appropriate workload type from the column on the left to its scenario on the right. Each workload type may be used once, more than once, or not at all. NOTE: Each correct selection is worth one point. Select and Place:Suggested Answer:
Box 1: Entity recognition - Named Entity Recognition (NER) is the ability to identify different entities in text and categorize them into pre-defined classes or types such as: person, location, event, product, and organization. Box 2: Sentiment analysis - Sentiment Analysis is the process of determining whether a piece of writing is positive, negative or neutral. Box 3: Translation - Using Microsoft's Translator text API This versatile API from Microsoft can be used for the following: Translate text from one language to another. Transliterate text from one script to another. Detecting language of the input text. Find alternate translations to specific text. Determine the sentence length. Reference: https://docs.microsoft.com/en-in/azure/cognitive-services/text-analytics/how-tos/text-analytics-how-to-entity-linking?tabs=version-3-preview https://azure.microsoft.com/en-us/services/cognitive-services/text-analytics
You need to build an app that will read recipe instructions aloud to support users who have reduced vision. Which version service should you use? A. Language service B. Translator C. Speech D. Personalizer Suggested Answer: C Speech, a managed service offering industry-leading speech capabilities such as speech-to-text, text-to-speech, speech translation, and speaker recognition. Reference: https://azure.microsoft.com/en-us/services/cognitive-services/speech-services/
You need to make the written press releases of your company available in a range of languages. Which service should you use? A. Translator B. Text Analytics C. Speech D. Language Understanding (LUIS) Suggested Answer: A Translator is a cloud-based machine translation service you can use to translate text in near real-time through a simple REST API call. The service uses modern neural machine translation technology and offers statistical machine translation technology. Custom Translator is an extension of Translator, which allows you to build neural translation systems. Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/translator/
You need to make the written press releases of your company available in a range of languages. Which service should you use? A. Speech B. Language C. Translator D. Personalizer Suggested Answer: C Translator, an AI service for real-time document and text translation. Translate text instantly or in batches across more than 100 languages, powered by the latest innovations in machine translation. Support a wide range of use cases, such as translation for call centers, multilingual conversational agents, or in-app communication. Reference: https://azure.microsoft.com/en-us/services/cognitive-services/translator/4
You are developing a chatbot solution in Azure. Which service should you use to determine a user's intent? A. Translator B. QnA Maker C. Speech D. Language Understanding (LUIS) Suggested Answer: D Language Understanding (LUIS) is a cloud-based API service that applies custom machine-learning intelligence to a user's conversational, natural language text to predict overall meaning, and pull out relevant, detailed information. Design your LUIS model with categories of user intentions called intents. Each intent needs examples of user utterances. Each utterance can provide data that needs to be extracted with machine-learning entities. Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/luis/what-is-luis
DRAG DROP - You plan to use Azure Cognitive Services to develop a voice controlled personal assistant app. Match the Azure Cognitive Services to the appropriate tasks. To answer, drag the appropriate service from the column on the left to its description on the right. Each service may be used once, more than once, or not at all. NOTE: Each correct selection is worth one point. Select and Place:Suggested Answer:
Box 1: Speech - The Speech service provides speech-to-text and text-to-speech capabilities with an Azure Speech resource. You can transcribe speech to text with high accuracy, produce natural-sounding text-to-speech voices, translate spoken audio, and use speaker recognition during conversations. Box 2: Language service - Build applications with conversational language understanding, a Cognitive Service for Language feature that understands natural language to interpret user goals and extracts key information from conversational phrases. Create multilingual, customizable intent classification and entity extraction models for your domain- specific keywords or phrases across 96 languages. Box 3: Speech - Incorrect: Not Translator text: Text translation is a cloud-based REST API feature of the Translator service that uses neural machine translation technology to enable quick and accurate source-to-target text translation in real time across all supported languages. Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/speech-service/overview https://azure.microsoft.com/en-us/services/cognitive-services/conversational-language-understanding/ https://docs.microsoft.com/en-us/azure/cognitive-services/translator/text-translation-overview
Which AI service can you use to interpret the meaning of a user input such as `Call me back later?` A. Translator B. Text Analytics C. Speech D. Language Understanding (LUIS) Suggested Answer: D Language Understanding (LUIS) is a cloud-based AI service, that applies custom machine-learning intelligence to a user's conversational, natural language text to predict overall meaning, and pull out relevant, detailed information. Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/luis/what-is-luis
You have an AI solution that provides users with the ability to control smart devices by using verbal commands. Which two types of natural language processing (NLP) workloads does the solution use? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point. A. text-to-speech B. key phrase extraction C. speech-to-text D. language modeling E. translation Suggested Answer: BC Key phrase extraction is one of the features offered by Azure Cognitive Service for Language, a collection of machine learning and AI algorithms in the cloud for developing intelligent applications that involve written language. Use key phrase extraction to quickly identify the main concepts in text. For example, in the text "The food was delicious and the staff were wonderful.", key phrase extraction will return the main topics: "food" and "wonderful staff". Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/language-service/key-phrase-extraction/overview
You have insurance claim reports that are stored as text. You need to extract key terms from the reports to generate summaries. Which type of AI workload should you use? A. natural language processing B. conversational AI C. anomaly detection D. computer vision Suggested Answer: A Reference: https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing
Which two scenarios are examples of a natural language processing workload? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point. A. monitoring the temperature of machinery to turn on a fan when the temperature reaches a specific threshold B. a smart device in the home that responds to questions such as, "What will the weather be like today?" C. a website that uses a knowledge base to interactively respond to users' questions D. assembly line machinery that autonomously inserts headlamps into cars Suggested Answer: BC Natural language processing (NLP) is used for tasks such as sentiment analysis, topic detection, language detection, key phrase extraction, and document categorization. Reference: https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing
HOTSPOT - For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point. Hot Area:Suggested Answer:
Box 1: Yes - Azure Cognitive Service for Language provides features including: * Language detection: This pre-configured feature evaluates text, and determines the language it was written in. It returns a language identifier and a score that indicates the strength of the analysis. Box 2: No - Handwritten detection is part of OCR (Optical Character Recognition). Box 3: Yes - Azure Cognitive Service for Language provides features including: * Named Entity Recognition (NER): This pre-configured feature identifies entities in text across several pre-defined categories. Note: Named entity recognition is a natural language processing technique that can automatically scan entire articles and pull out some fundamental entities in a text and classify them into predefined categories. Entities may be, Organizations, Quantities, Monetary values, Percentages, and more. People's names - Company names - Geographic locations (Both physical and political) Product names - Dates and times - Amounts of money - Names of events - Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/language-service/overview
You are building a Language Understanding model for an e-commerce business. You need to ensure that the model detects when utterances are outside the intended scope of the model. What should you do? A. Test the model by using new utterances B. Add utterances to the None intent C. Create a prebuilt task entity D. Create a new model Suggested Answer: B The None intent is filled with utterances that are outside of your domain. Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/LUIS/luis-concept-intent
You need to develop a web-based AI solution for a customer support system. Users must be able to interact with a web app that will guide them to the best resource or answer. Which service should you use? A. Custom Vision B. QnA Maker C. Translator Text D. Face Suggested Answer: B QnA Maker is a cloud-based API service that lets you create a conversational question-and-answer layer over your existing data. Use it to build a knowledge base by extracting questions and answers from your semi-structured content, including FAQs, manuals, and documents. Answer users' questions with the best answers from the QnAs in your knowledge baseג€"automatically. Your knowledge base gets smarter, too, as it continually learns from user behavior. Incorrect Answers: A: Azure Custom Vision is a cognitive service that lets you build, deploy, and improve your own image classifiers. An image classifier is an AI service that applies labels (which represent classes) to images, according to their visual characteristics. Unlike the Computer Vision service, Custom Vision allows you to specify the labels to apply. D: Azure Cognitive Services Face Detection API: At a minimum, each detected face corresponds to a faceRectangle field in the response. This set of pixel coordinates for the left, top, width, and height mark the located face. Using these coordinates, you can get the location of the face and its size. In the API response, faces are listed in size order from largest to smallest. Reference: https://azure.microsoft.com/en-us/services/cognitive-services/qna-maker/
Which two scenarios are examples of a conversational AI workload? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point. A. a smart device in the home that responds to questions such as ג€What will the weather be like today?ג€ B. a website that uses a knowledge base to interactively respond to users' questions C. assembly line machinery that autonomously inserts headlamps into cars D. monitoring the temperature of machinery to turn on a fan when the temperature reaches a specific threshold Suggested Answer: AB
You need to reduce the load on telephone operators by implementing a chatbot to answer simple questions with predefined answers. Which two AI service should you use to achieve the goal? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point. A. Text Analytics B. QnA Maker C. Azure Bot Service D. Translator Suggested Answer: BC Bots are a popular way to provide support through multiple communication channels. You can use the QnA Maker service and Azure Bot Service to create a bot that answers user questions. Reference: https://docs.microsoft.com/en-us/learn/modules/build-faq-chatbot-qna-maker-azure-bot-service/
You have a frequently asked questions (FAQ) PDF file. You need to create a conversational support system based on the FAQ. Which service should you use? A. QnA Maker B. Text Analytics C. Computer Vision D. Language Understanding (LUIS) Suggested Answer: A QnA Maker is a cloud-based API service that lets you create a conversational question-and-answer layer over your existing data. Use it to build a knowledge base by extracting questions and answers from your semi-structured content, including FAQs, manuals, and documents. Reference: https://azure.microsoft.com/en-us/services/cognitive-services/qna-maker/
You need to provide content for a business chatbot that will help answer simple user queries. What are three ways to create question and answer text by using QnA Maker? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point. A. Generate the questions and answers from an existing webpage. B. Use automated machine learning to train a model based on a file that contains the questions. C. Manually enter the questions and answers. D. Connect the bot to the Cortana channel and ask questions by using Cortana. E. Import chit-chat content from a predefined data source. Suggested Answer: ACE Automatic extraction - Extract question-answer pairs from semi-structured content, including FAQ pages, support websites, excel files, SharePoint documents, product manuals and policies. Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/qnamaker/concepts/content-types
You have a knowledge base of frequently asked questions (FAQ). You create a bot that uses the knowledge base to respond to customer requests. You need to identify what the bot can perform without adding additional skills. What should you identify? A. Register customer purchases. B. Register customer complaints. C. Answer questions from multiple users simultaneously. D. Provide customers with return materials authorization (RMA) numbers. Suggested Answer: C Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/qnamaker/overview/overview
You are developing a conversational AI solution that will communicate with users through multiple channels including email, Microsoft Teams, and webchat. Which service should you use? A. Text Analytics B. Azure Bot Service C. Translator D. Form Recognizer Suggested Answer: B Reference: https://docs.microsoft.com/en-us/azure/bot-service/bot-service-overview-introduction?view=azure-bot-service-4.0
Which two scenarios are examples of a conversational AI workload? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point. A. a telephone answering service that has a pre-recorder message B. a chatbot that provides users with the ability to find answers on a website by themselves C. telephone voice menus to reduce the load on human resources D. a service that creates frequently asked questions (FAQ) documents by crawling public websites Suggested Answer: BC B: A bot is an automated software program designed to perform a particular task. Think of it as a robot without a body. C: Automated customer interaction is essential to a business of any size. In fact, 61% of consumers prefer to communicate via speech, and most of them prefer self-service. Because customer satisfaction is a priority for all businesses, self-service is a critical facet of any customer-facing communications strategy. Incorrect Answers: D: Early bots were comparatively simple, handling repetitive and voluminous tasks with relatively straightforward algorithmic logic. An example would be web crawlers used by search engines to automatically explore and catalog web content. Reference: https://docs.microsoft.com/en-us/azure/architecture/data-guide/big-data/ai-overview https://docs.microsoft.com/en-us/azure/architecture/solution-ideas/articles/interactive-voice-response-bot
You have a webchat bot that provides responses from a QnA Maker knowledge base. You need to ensure that the bot uses user feedback to improve the relevance of the responses over time. What should you use? A. key phrase extraction B. sentiment analysis C. business logic D. active learning Suggested Answer: D Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/qnamaker/how-to/improve-knowledge-base
HOTSPOT - For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point. Hot Area:Suggested Answer:
Box 1: Yes - Azure bot service can be integrated with the powerful AI capabilities with Azure Cognitive Services. Box 2: Yes - Azure bot service engages with customers in a conversational manner. Box 3: No - The QnA Maker service creates knowledge base, not question and answers sets. Note: You can use the QnA Maker service and a knowledge base to add question-and-answer support to your bot. When you create your knowledge base, you seed it with questions and answers. Reference: https://docs.microsoft.com/en-us/azure/bot-service/bot-builder-tutorial-add-qna
HOTSPOT - For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point. Hot Area:Suggested Answer:
Reference: https://docs.microsoft.com/en-us/azure/bot-service/bot-service-overview-introduction?view=azure-bot-service-4.0
HOTSPOT - For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point. Hot Area:Suggested Answer:
Reference: https://docs.microsoft.com/en-us/azure/architecture/reference-architectures/ai/conversational-bot https://docs.microsoft.com/en-us/azure/bot-service/bot-builder-webchat-overview?view=azure-bot-service-4.0
You have insurance claim reports that are stored as text. You need to extract key terms from the reports to generate summaries. Which type of AI workload should you use? A. anomaly detection B. natural language processing C. computer vision D. knowledge mining Suggested Answer: B Key phrase extraction is one of the features offered by Azure Cognitive Service for Language, a collection of machine learning and AI algorithms in the cloud for developing intelligent applications that involve written language. Use key phrase extraction to quickly identify the main concepts in text. For example, in the text "The food was delicious and the staff were wonderful.", key phrase extraction will return the main topics: "food" and "wonderful staff". Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/language-service/key-phrase-extraction/overview
HOTSPOT - For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point. Hot Area:Suggested Answer:
Reference: https://docs.microsoft.com/en-us/azure/bot-service/bot-service-manage-channels?view=azure-bot-service-4.0
HOTSPOT - For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point. Hot Area:Suggested Answer:
Reference: https://docs.microsoft.com/en-gb/azure/cognitive-services/qnamaker/concepts/data-sources-and-content https://docs.microsoft.com/en-us/azure/cognitive-services/luis/choose-natural-language-processing-service
HOTSPOT - For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point. Hot Area:Suggested Answer:
Reference: https://docs.microsoft.com/en-us/azure/bot-service/bot-service-overview-introduction?view=azure-bot-service-4.0
HOTSPOT - To complete the sentence, select the appropriate option in the answer area. Hot Area:Suggested Answer:
With Microsoft's Conversational AI tools developers can build, connect, deploy, and manage intelligent bots that naturally interact with their users on a website, app, Cortana, Microsoft Teams, Skype, Facebook Messenger, Slack, and more. Reference: https://azure.microsoft.com/en-in/blog/microsoft-conversational-ai-tools-enable-developers-to-build-connect-and-manage-intelligent-bots
Which scenario is an example of a webchat bot? A. Determine whether reviews entered on a website for a concert are positive or negative, and then add a thumbs up or thumbs down emoji to the reviews. B. Translate into English questions entered by customers at a kiosk so that the appropriate person can call the customers back. C. Accept questions through email, and then route the email messages to the correct person based on the content of the message. D. From a website interface, answer common questions about scheduled events and ticket purchases for a music festival. Suggested Answer: D
You have the process shown in the following exhibit.Which type of AI solution is shown in the diagram? A. a sentiment analysis solution B. a chatbot C. a machine learning model D. a computer vision application Suggested Answer: B
Which AI service should you use to create a bot from a frequently asked questions (FAQ) document? A. QnA Maker B. Language Understanding (LUIS) C. Text Analytics D. Speech Suggested Answer: A
Which statement is an example of a Microsoft responsible AI principle? A. AI systems must use only publicly available data B. AI systems must be transparent and inclusive C. AI systems must keep personal details public D. AI systems must protect the interests of the company Suggested Answer: B
DRAG DROP - Match the types of AI workloads to the appropriate scenarios. To answer, drag the appropriate workload type from the column on the left to its scenario on the right. Each workload type may be used once, more than once, or not at all. NOTE: Each correct selection is worth one point.Suggested Answer:
You have an Azure Machine Learning pipeline that contains a Split Data module. The Split Data module outputs to a Train Model module and a Score Model module. What is the function of the Split Data module? A. scaling numeric variables so that they are within a consistent numeric range B. creating training and validation datasets C. diverting records that have missing data D. selecting columns that must be included in the model Suggested Answer: B
DRAG DROP - Match the tool to the Azure Machine Learning task. To answer, drag the appropriate tool from the column on the left to its tasks on the right. Each tool may be used once, more than once, or not at all. NOTE: Each correct selection is worth one point.Suggested Answer:
HOTSPOT - Select the answer that correctly completes the sentence. Hot Area:Suggested Answer:
Azure's Computer Vision service gives you access to advanced algorithms that process images and return information based on the visual features you're interested in. * Optical Character Recognition (OCR) * Spatial Analysis * Image Analysis The Image Analysis service extracts many visual features from images, such as objects, faces, adult content, and auto-generated text descriptions. Follow the Image Analysis quickstart to get started. Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/overview
You use Azure Machine Learning designer to build a model pipeline. What should you create before you can run the pipeline? A. a registered model B. a compute resource C. a Jupyter notebook Suggested Answer: B
HOTSPOT - For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point. Hot Area:Suggested Answer:
Box 1: No - Build conversational experiences with Power Virtual Agents and Azure Bot Service Azure Bot Service provides an integrated development environment for bot building. Its integration with Power Virtual Agents, a fully hosted low-code platform, enables developers of all technical abilities build conversational AI botsג€"no code needed. Box 2: Yes - Box 3: Yes - You can configure your bot to communicate with people via Microsoft Teams. Reference: https://azure.microsoft.com/en-us/services/bot-services/#overview https://docs.microsoft.com/en-us/azure/bot-service/channel-connect-teams
You have a dataset. You need to build an Azure Machine Learning classification model that will identify defective products. What should you do first? A. Load the dataset. B. Create a clustering model. C. Split the data into training and testing datasets. D. Create a classification model. Suggested Answer: C
HOTSPOT - For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point. Hot Area:Suggested Answer:
Box 1: Yes - You can create and build a cortana bot using microsoft bot framework. Note: Connect Cortana Channels - Login to Azure portal > Select the ג€All Resourcesג€ > Select Channels > Select Cortana icon. Let us start to configure the ג€Cortana ג€Channel and follow the below steps, at the end of this article you will be able to deploy the Bot into the Cortana. Etc. Box 2: Yes - QnA Maker is an easy-to-use web-based service that makes it easy to power a question-answer application or chatbot from semi-structured content like FAQ documents and product manuals. With QnA Maker, developers can build, train, and publish question and answer bots in minutes. Box 3: Yes - Reference: https://www.c-sharpcorner.com/article/create-and-build-a-cortana-bot-using-microsoft-bot-framework/
HOTSPOT - Select the answer that correctly completes the sentence.Suggested Answer:
You are developing a system to predict the prices of insurance for drivers in the United Kingdom. You need to minimize bias in the system. What should you do? A. Remove information about protected characteristics from the data before sampling. B. Take a training sample that is representative of the population in the United Kingdom. C. Create a training dataset that uses data from global insurers. D. Take a completely random training sample. Suggested Answer: C
You have an AI-based loan approval system. During testing, you discover that the system has a gender bias. Which responsible Al principle does this violate? A. accountability B. reliability and safety C. transparency D. fairness Suggested Answer: D
Which machine learning technique can be used for anomaly detection? A. A machine learning technique that classifies objects based on user supplied images. B. A machine learning technique that understands written and spoken language. C. A machine learning technique that classifies images based on their contents. D. A machine learning technique that analyzes data over time and identifies unusual changes. Suggested Answer: D
DRAG DROP - Match the services to the appropriate descriptions. To answer, drag the appropriate service from the column on the left to its description on the right. Each service may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content. NOTE: Each correct selection is worth one point.Suggested Answer:
HOTSPOT - Select the answer that correctly completes the sentence.Suggested Answer:
You have a natural language processing (NLP) model that was created by using data obtained without permission. Which Microsoft principle for responsible AI does this breach? A. reliability and safety B. privacy and security C. inclusiveness D. transparency Suggested Answer: D
During the process of Machine Learning, when should you review evaluation metrics. A. Before you train a model. B. After you clean the data. C. Before you choose the type of model. D. After you test a model on the validation data. Suggested Answer: D
DRAG DROP - Match the principles of responsible AI to the appropriate descriptions. To answer, drag the appropriate principle from the column on the left to its description on the right. Each principle may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content. NOTE: Each correct selection is worth one point.Suggested Answer:
You need to reduce the load on telephone operators by implementing a chatbot to answer simple questions with predefined answers. Which two AI services should you use to achieve the goal? Each correct answers presents part of the solution. NOTE: Each correct selection is worth one point. A. Azure Machine Learning B. Azure Bot Service C. Language Service D. Translator Suggested Answer: AB
DRAG DROP - Match the types of natural language processing workloads to the appropriate scenarios. To answer, drag the appropriate workload type from the column on the left to its scenario on the right. Each workload type may be used once, more than once, or not at all. NOTE: Each correct match is worth one point. Select and Place:Suggested Answer:
HOTSPOT - Select the answer that correctly completes the sentence.Suggested Answer:
You need to create a customer support solution to help customers access information. The solution must support email, phone, and live chat channels. Which type of Al solution should you use? A. machine learning B. computer vision C. chatbot D. natural language processing (NLP) Suggested Answer: C
You need to create a model that labels a collection of your personal digital photographs. Which Azure Cognitive Services service should you use? A. Form Recognizer B. Custom Vision C. Language D. Computer Vision Suggested Answer: D
HOTSPOT - For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.Suggested Answer:
A historian can use ________ to digitize newspaper articles. Select the answer that correctly completes the sentence. A. Object detection B. Facial recognition C. Image classification D. Optical character recognition (OCR) Suggested Answer: D
HOTSPOT - Select the answer that correctly completes the sentence.Suggested Answer:
You need to identify groups of rows with similar numeric values in a dataset. Which type of machine learning should you use? A. clustering B. regression C. classification Suggested Answer: A
HOTSPOT - Select the answer that correctly completes the sentence.Suggested Answer:
You need to track multiple versions of a model that was trained by using Azure Machine Learning. What should you do? A. Explain the model. B. Register the model. C. Register the training data. D. Provision an inference cluster. Suggested Answer: B
You have a dataset that contains the columns shown in the following table.You have a machine learning model that predicts the value of ColumnE based on the other numeric columns. Which type of model is this? A. analysis B. clustering C. regression Suggested Answer: C
You need to create a clustering model and evaluate the model by using Azure Machine Learning designer. What should you do? A. Split the original dataset into a dataset for training and a dataset for testing. Use the testing dataset for evaluation. B. Use the original dataset for training and evaluation. C. Split the original dataset into a dataset for features and a dataset for labels. Use the features dataset for evaluation. D. Split the original dataset into a dataset for training and a dataset for testing. Use the training dataset for evaluation. Suggested Answer: A
HOTSPOT - Select the answer that correctly completes the sentence.Suggested Answer:
HOTSPOT - For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.Suggested Answer:
DRAG DROP - You plan to deploy an Azure Machine Learning model by using the Machine Learning designer. Which four actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.Suggested Answer:
DRAG DROP - Match the Azure Cognitive Services to the appropriate actions. To answer, drag the appropriate service from the column on the left to its action on the right. Each service may be used once, more than once, or not at all. NOTE: Each correct selection is worth one point.Suggested Answer:
HOTSPOT - Select the answer that correctly completes the sentence.Suggested Answer:
HOTSPOT - Select the answer that correctly completes the sentence.Suggested Answer:
In a machine learning model, the data that is used as inputs are called ________. Select the answer that correctly completes the sentence. A. dataset B. labels C. variables Suggested Answer: B
HOTSPOT - Select the answer that correctly completes the sentence.Suggested Answer:
Predicting how many vehicles will travel across a bridge on a give day is an example of _______. Select the answer that correctly completes the sentence. A. regression B. translation C. classification D. clustering Suggested Answer: A
You are building a chatbot that will use natural language processing (NLP) to perform the following actions based on the text input of a user. • Accept customer orders. • Retrieve support documents. • Retrieve order status updates. Which type of NLP should you use? A. sentiment analysis B. named entity recognition C. translation D. language modeling Suggested Answer: B
An app that analyzes social media posts to identify their tone is an example of which type of natural language processing (NLP) workload? A. sentiment analysis B. speech recognition C. key phrase extraction D. entity recognition Suggested Answer: A
HOTSPOT - For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.Suggested Answer:
You plan to develop a bot that will enable users to query a knowledge base by using natural language processing. Which two services should you include in the solution? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point. A. Language Service B. Azure Bot Service C. Form Recognizer D. Anomaly Detector Suggested Answer: AB
You need to implement a pre-built solution that will identify well-known brands in digital photographs. Which Azure Cognitive Services service should you use? A. Custom Vision B. Form Recognizer C. Face D. Computer Vision Suggested Answer: D
Natural language processing can be used to __________. Select the answer that correctly completes the sentence. A. Analyze video content B. Generate speech C. Classify email messages as work-related or personal. D. Classify images Suggested Answer: C
DRAG DROP - Match the Azure Cognitive Services to the appropriate AI workloads. To answer, drag the appropriate service from the column on the left to its workload on the right. Each service may be used once, more than once, or not at all. NOTE: Each correct match is worth one point.Suggested Answer:
HOTSPOT - To complete the sentence, select the appropriate option in the answer area.Suggested Answer:
You need to develop a mobile app for employees to scan and store their expenses while travelling. Which type of computer vision should you use? A. face detection B. image classification C. object detection D. optical character recognition (OCR) Suggested Answer: D
HOTSPOT - For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.Suggested Answer:
HOTSPOT - Select the answer that correctly completes the sentence.Suggested Answer:
HOTSPOT - For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.Suggested Answer:
HOTSPOT - Select the answer that correctly completes the sentence.Suggested Answer:
You have a solution that analyzes social media posts to extract the mentions of city names and the city names discussed most frequently. Which type of natural language processing (NLP) workload does the solution use? A. speech recognition B. sentiment analysis C. key phrase extraction D. entity recognition Suggested Answer: C
You need to convert handwritten notes into digital text. Which type of computer vision should you use? A. facial detection B. optical character recognition (OCR) C. image classification D. object detection Suggested Answer: B
HOTSPOT - For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.Suggested Answer:
Your company manufactures widgets. You have 1,000 digital photos of the widgets. You need to identify the location of the widgets within the photos. What should you use? A. Computer Vision Spatial Analysis B. Custom Vision object detection C. Computer Vision Image Analysis D. Custom Vision classification Suggested Answer: B
You have a website that includes customer reviews. You need to store the reviews in English and present the reviews to users in their respective language by recognizing each user's geographical location. Which type of natural language processing workload should you use? A. key phrase extraction B. speech recognition C. language modeling D. translation Suggested Answer: A
HOTSPOT - For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.Suggested Answer:
DRAG DROP - Match the Azure Cognitive Services service to the appropriate actions. To answer, drag the appropriate service from the column on the left to its action on the right. Each service may be used once, more than once, or not at all. NOTE: Each correct selection is worth one point.Suggested Answer:
HOTSPOT - Select the answer that correctly completes the sentence.Suggested Answer:
HOTSPOT - Select the answer that correctly completes the sentence.Suggested Answer:
You need to convert receipts into transactions in a spreadsheet. The spreadsheet must include the date of the transaction, the merchant, the total spent, and any taxes paid. Which Azure AI service should you use? A. Custom Vision B. Form Recognizer C. Face D. Language Suggested Answer: B
HOTSPOT - You have a large dataset that contains motor vehicle sales data. You need to train an automated machine learning (automated ML) model to predict vehicle sale values based on the type of vehicle. Which task should you select? To answer, select the appropriate task in the answer area. NOTE: Each correct selection is worth one point.Suggested Answer:
Your company is exploring the use of voice recognition technologies in its smart home devices. The company wants to identify any barriers that might unintentionally leave out specific user groups. This is an example of which Microsoft guiding principle for responsible AI? A. accountability B. fairness C. privacy and security D. inclusiveness Suggested Answer: D
You have 100 instructional videos that do NOT contain any audio. Each instructional video has a script. You need to generate a narration audio file for each video based on the script. Which type of workload should you use? A. language modeling B. speech recognition C. speech synthesis D. translation Suggested Answer: C
HOTSPOT - To complete the sentence, select the appropriate option in the answer area.Suggested Answer:
You have a solution that reads manuscripts in different languages and categorizes the manuscripts based on topic. Which types of natural language processing (NLP) workloads does the solution use? A. speech recognition and entity recognition B. speech recognition and language modeling C. translation and key phrase extraction D. translation and sentiment analysis Suggested Answer: C
DRAG DROP - You are designing a system that will generate insurance quotes automatically. Match the Microsoft responsible AI principles to the appropriate requirements. To answer, drag the appropriate principle from the column on the left to its requirement on the right. Each principle may be used once, more than once, or not at all. NOTE: Each correct match is worth one point.Suggested Answer:
HOTSPOT - Select the answer that correctly completes the sentence.Suggested Answer:
Which type of natural language processing (NLP) entity is used to identify a phone number? A. regular expression B. machine-learned C. list D. Pattern.any Suggested Answer: C
HOTSPOT - You have an app that identifies birds in images. The app performs the following tasks: • Identifies the location of the birds in the image • Identifies the species of the birds in the image Which type of computer vision does each task use? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.Suggested Answer:
You are developing a solution that uses the Language service. You need to identify the main talking points in a collection of documents. Which type of natural language processing should you use? A. language detection B. sentiment analysis C. entity recognition D. key phrase extraction Suggested Answer: D
HOTSPOT - Select the answer that correctly completes the sentence.Suggested Answer:
HOTSPOT - Select the answer that correctly completes the sentence.Suggested Answer:
You need to build an image tagging solution for social media that tags images of your friends automatically. Which Azure Cognitive Services service should you use? A. Face B. Form Recognizer C. Language D. Computer Vision Suggested Answer: A
You have an app that identifies the coordinates of a product in an image of a supermarket shelf. Which service does the app use? A. Custom Vision classification B. Custom Vision object detection C. Computer Vision Read D. Computer Vision optical character recognition (OCR) Suggested Answer: B
For which two workloads can you use computer vision? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point. A. assigning the color pixels in an image to object names B. detecting inconsistencies and anomalies in a stream of data C. creating visual representations of numerical data D. creating photorealistic images by using three-dimensional models E. describing the contents of an image Suggested Answer: AE
You have a security system that analyzes images from CCTV to provide authorized staff entry into restricted area. Which type of computer vision does the system use? A. optical character recognition (OCR) B. semantic segmentation C. facial detection and facial recognition D. image analysis Suggested Answer: C
HOTSPOT - Select the answer that correctly completes the sentence.Suggested Answer:
HOTSPOT - Select the answer that correctly completes the sentence.Suggested Answer:
HOTSPOT - Select the answer that correctly completes the sentence.Suggested Answer:
Which AI service can you use to extract intent from a user input such as “Call me back later”? A. Azure Cognitive Search B. Translator C. Language D. Speech Suggested Answer: D
HOTSPOT - Select the answer that correctly completes the sentence.Suggested Answer:
You are building a Language Understanding model for an e-commerce business. You need to ensure that the model detects when utterances are outside the intended scope of the model. What should you do? A. Export the model B. Add utterances to the None intent C. Create a prebuilt task entity D. Create a new model Suggested Answer: B
Predicting agricultural yields based on weather conditions and soil quality measurements is an example of which type of machine learning model? A. classification B. regression C. clustering Suggested Answer: B
HOTSPOT - For each of the following statement, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.Suggested Answer:
You have a bot that identifies the brand names of products in images of supermarket shelves. Which service does the bot use? A. AI enrichment for Azure Search capabilities B. Computer Vision Image Analysis capabilities C. Custom Vision Image Classification capabilities D. Language Understanding capabilities Suggested Answer: C
HOTSPOT - Select the answer that correctly completes the sentence.Suggested Answer:
DRAG DROP - Match the machine learning models to the appropriate descriptions. To answer, drag the appropriate model from the column on the left to its description on the right. Each model may be used once, more than once, or not at all. NOTE: Each correct match is worth one point.Suggested Answer:
You are building a tool that will process images from retail stores and identify the products of competitors. The solution must be trained on images provided by your company. Which Azure AI service should you use? A. Form Recognizer B. Custom Vision C. Face D. Computer Vision Suggested Answer: B
DRAG DROP - Match the types of computer vision workloads to the appropriate scenarios. To answer, drag the appropriate workload type from the column on the left to its scenario on the right. Each workload type may be used once, more than once, or not at all. NOTE: Each correct selection is worth one point.Suggested Answer:
You need to identify street names based on street signs in photographs. Which type of computer vision should you use? A. object detection B. optical character recognition (OCR) C. image classification D. facial recognition Suggested Answer: A
You are developing a chatbot solution in Azure. Which service should you use to determine a user’s intent? A. Translator B. Language C. Azure Cognitive Search D. Speech Suggested Answer: B
A smart device that responds to the question “What is the stock price of Contoso. Ltd.?” is an example of which AI workload? A. knowledge mining B. natural language processing C. computer vision D. anomaly detection Suggested Answer: A
HOTSPOT - For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.Suggested Answer:
You need to provide customers with the ability to query the status of orders by using phones, social media, or digital assistants. What should you use? A. an Azure Machine Learning model B. the Translator service C. a Form Recognizer model D. Azure Bot Service Suggested Answer: D
Which Azure Cognitive Services service can be used to identify documents that contain sensitive information? A. Custom Vision B. Conversational Language Understanding C. Form Recognizer Suggested Answer: C
What is an example of the Microsoft responsible AI principle of transparency? A. ensuring that opportunities are allocated equally to all applicants B. helping users understand the decisions made by an AI system C. ensuring that developers are accountable for the solutions they create D. ensuring that the privileged data of users is stored in a secure manner Suggested Answer: B
You plan to build a conversational AI solution that can be surfaced in Microsoft Teams, Microsoft Cortana, and Amazon Alexa. Which service should you use? A. Azure Bot Service B. Azure Cognitive Search C. Speech D. Language service Suggested Answer: A
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