HOTSPOT - You are a lead data scientist for a project that tracks the health and migration of birds. You create a multi-image classification deep learning model that uses a set of labeled bird photos collected by experts. You plan to use the model to develop a cross-platform mobile app that predicts the species of bird captured by app users. You must test and deploy the trained model as a web service. The deployed model must meet the following requirements: ✑ An authenticated connection must not be required for testing. ✑ The deployed model must perform with low latency during inferencing. ✑ The REST endpoints must be scalable and should have a capacity to handle large number of requests when multiple end users are using the mobile application. You need to verify that the web service returns predictions in the expected JSON format when a valid REST request is submitted. Which compute resources should you use? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point. Hot Area: Suggested Answer:
Box 1: ds-workstation notebook VM An authenticated connection must not be required for testing. On a Microsoft Azure virtual machine (VM), including a Data Science Virtual Machine (DSVM), you create local user accounts while provisioning the VM. Users then authenticate to the VM by using these credentials. Box 2: gpu-compute cluster - Image classification is well suited for GPU compute clusters Reference: https://docs.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/dsvm-common-identity https://docs.microsoft.com/en-us/azure/architecture/reference-architectures/ai/training-deep-learning This question is in DP-100 Exam For getting Microsoft Azure Data Scientist Associate Certificate Disclaimers: The website is not related to, affiliated with, endorsed or authorized by Microsoft. The website does not contain actual questions and answers from Microsoft's Certification Exams. Trademarks, certification & product names are used for reference only and belong to Microsoft.
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