Home » Microsoft » DP-100 v.2 » What should you do?
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 influencing 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
Correct Answer: A
Explanation/Reference:
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-learningpipelines/parallel-run/README.md
