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Exam AI-300 Topic 1 Question 75 Discussion

Actual exam question for Microsoft's AI-300 exam
Question #: 75
Topic #: 1
You manage an Azure Machine learning workspace. You develop a machine learning model.
You must deploy the model to use a low-priority VM with a pricing discount.
You need to deploy the model.
Which compute target should you use?

Suggested Answer: B Vote an answer

The best compute target for deploying a model using low-priority VMs (or their modern successor, Spot VMs) is an Azure Machine Learning compute cluster.
Best Compute Target: AML Compute Cluster
For low-priority/Spot pricing, you should use an Azure Machine Learning compute cluster configured with the LowPriority tier.
Primary Use Case: This target is specifically recommended for batch deployments. Batch inference is ideal for low-priority VMs because these jobs are asynchronous and can tolerate the interruptions (preemptions) inherent to discounted capacity.
Pricing Advantage: Low-priority VMs offer significant discounts-often up to 80% off standard rates-by utilizing unused Azure capacity.
Automatic Handling: When a node is preempted during a batch job, Azure Machine Learning automatically attempts to replace the lost capacity and re-queues failed tasks to the cluster.
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-low-priority-batch

by Howar at Jun 27, 2026, 07:24 AM

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