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AI-200 Security And Observability Practice Question

Your machine learning engineering team needs to run training jobs on Azure Machine Learning compute clusters. To minimize costs during off-peak hours, you need to configure the compute cluster to automatically scale down to zero nodes when no jobs are queued, and scale up when jobs are submitted. What setting should you configure on the compute cluster?

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

Set the minimum number of nodes to 0 and the maximum nodes according to workload requirements

Configuring the minimum number of nodes to 0 on an Azure Machine Learning compute cluster allows it to scale down completely when idle, reducing costs.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • Set the minimum number of nodes to 0 and the maximum nodes according to workload requirements

    Why this is correct

    Correct. Setting min nodes to 0 allows compute resources to deallocate when idle, saving costs.

  • Configure Azure Automation runbooks to stop the physical datacenter servers

    Why it's wrong here

    Incorrect. Customers do not manage physical datacenter servers in Azure.

  • Set the minimum number of nodes equal to the maximum number of nodes

    Why it's wrong here

    Incorrect. This keeps all nodes running continuously, preventing scale-down and increasing costs.

  • Use Azure Functions timer triggers to delete the compute target every evening

    Why it's wrong here

    Incorrect. Recreating compute targets daily is inefficient; native autoscaling from min=0 handles idle scaling automatically.

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JA

Written and reviewed by Johnson Ajibi, MSc IT Security

Senior Network & Security Engineer · founder of Courseiva

Last reviewed August 2026 · checked against the official Microsoft exam blueprint

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