You use the Azure Machine Learning designer to create and run a training pipeline. The pipeline must be run every night to inference predictions from a large volume of files. The folder where the files will be stored is defined as a dataset. You need to publish the pipeline as a REST service that can be used for the nightly inferencing run. What should you do? A. Create a batch inference pipeline B. Set the compute target for the pipeline to an inference cluster C. Create a real-time inference pipeline D. Clone the pipeline  Suggested Answer: A Azure Machine Learning Batch Inference targets large inference jobs that are not time-sensitive. Batch Inference provides cost-effective inference compute scaling, with unparalleled throughput for asynchronous applications. It is optimized for high-throughput, fire-and-forget inference over large collections of data. You can submit a batch inference job by pipeline_run, or through REST calls with a published pipeline. Reference: https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/machine-learning-pipelines/parallel-run/README.md 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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