DP-750: Implementing Data Engineering Solutions Using Azure DatabricksDemo

By Microsoft · Question Mode

QuestionQ42

Set up and configure an Azure Databricks environment

You have an Azure Databricks workspace that includes a cluster named Cluster1.

Performance monitoring indicates that Cluster1 is consistently overprovisioned for its batch workload:

  • CPU utilization stays below 20 percent, even during peak processing periods.
  • The workload is highly predictable and has no spikes.
  • The current node type already satisfies the workload requirements.

You need to lower compute costs without increasing job duration. What should you do?

  • A Enable Photon acceleration.
  • B Configure Cluster1 to use a larger node type.
  • C Decrease the autotermination timeout of Cluster1.
  • D Disable autoscaling and reduce the number of worker nodes.
Explanation

A predictable batch workload that remains underutilized even at peak can be right-sized by using fewer worker nodes while retaining the existing node type. This removes unused compute capacity and lowers cost without increasing duration when the reduced worker count still meets the workload’s demonstrated peak requirements. Azure Databricks autoscaling is intended to add and remove workers as workload characteristics change; it is not needed for a stable workload with a known adequate fixed size.

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