Answer-first summary for fast verification
Answer: AmlCompute
The correct answer is C (AmlCompute) because it is the recommended and most suitable compute target for ParallelRunStep in Azure Machine Learning batch inference pipelines. AmlCompute provides managed compute infrastructure that supports distributed processing and parallel execution, which aligns perfectly with the requirements of ParallelRunStep for efficient batch scoring. The community discussion confirms this with 100% consensus on option C, and detailed comments explain that AmlCompute is specifically designed for machine learning workloads with scalable VM sizes. While AKSCompute (option D) can handle distributed processing, it's optimized for real-time inference with low latency requirements, making it less suitable and more costly for batch inference scenarios where parallel processing efficiency is the primary concern. BatchCompute (A) and AdlaCompute (B) are not valid Azure ML compute target classes for this purpose.
Author: LeetQuiz Editorial Team
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You create and register a model in an Azure Machine Learning workspace. You plan to use the Azure Machine Learning SDK to implement a batch inference pipeline that uses a ParallelRunStep to score input data with the model. You must specify a value for the ParallelRunConfig compute_target setting for the pipeline step.
You need to create the compute target. Which class should you use?
A
BatchCompute
B
AdlaCompute
C
AmlCompute
D
AksCompute
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