Exam DP-100 topic 4 question 35 discussion

Actual exam question from Microsoft's DP-100

Question #: 35
Topic #: 4

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

Show Suggested Answer

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

by JTWang at Oct. 24, 2022, 6:04 a.m.

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BTAB

11 months, 3 weeks ago

Correct

upvoted 1 times

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JTWang

1 year, 2 months ago

Selected Answer: A

Correct.

upvoted 2 times

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