A team runs a mix of SQL analytics and Python machine learning workloads on the same Azure Databricks cluster. They want to improve SQL query performance without affecting the Python workloads. They also need to ensure that the cluster can handle variable workloads. Which configuration should they use?

Enable autoscaling and increase the maximum node count.

Use a separate cluster for SQL workloads with Photon enabled, and keep the existing cluster for Python workloads.

Enable Photon acceleration on the cluster.

Use a larger node type for the cluster.

A machine learning team needs to train models using distributed training with GPU acceleration. They plan to use an interactive cluster for experimentation. Which compute configuration should they choose?

Create a job compute cluster with CPU nodes and enable Photon.

Create an all-purpose cluster with GPU node types and enable ML runtime.

Use a SQL warehouse with GPU nodes.

Create a classic cluster with CPU nodes and install ML libraries manually.

A company runs a mix of scheduled ETL jobs and interactive ad-hoc analysis. They want to minimize costs while ensuring that interactive analysis is not impacted by job workloads. They also want to avoid managing multiple clusters. Which approach should they take?

Use a single all-purpose cluster for both jobs and interactive analysis, with autoscaling.

Use a single job compute cluster for both jobs and interactive analysis.

Use job compute clusters for scheduled jobs and a separate all-purpose cluster for interactive analysis.

Use a SQL warehouse for both jobs and interactive analysis.

Microsoft Certified:Azure Databricks Data Engineer Associate

Domain 1Objective 1

Select and Configure Compute in a Workspace DP-750 Practice Questions (Page 4)

Part of the Set up and configure an Azure Databricks environment domain, which accounts for 15–20% of the DP-750 exam. Microsoft does not publish an official question count, but from its 120-minute exam (~50–80 total, ~8–16 in this domain), expect 4–8 from this objective — we provide 27 practice questions to prepare you well beyond it. (estimate)

27questions here

6free pages

5concepts

15–20%of the exam

Questions 16–20

16expert · hard
A team runs a mix of SQL analytics and Python machine learning workloads on the same Azure Databricks cluster. They want to improve SQL query performance without affecting the Python workloads. They also need to ensure that the cluster can handle variable workloads. Which configuration should they use?
Select an answer first
17foundation · easy
A data engineer is configuring a classic compute cluster for a workload that has unpredictable demand. The engineer wants the cluster to automatically add and remove workers based on the current workload. Which configuration setting should the engineer enable?
Select an answer first
18foundation · easy
A data engineer needs to run a scheduled production batch job that processes data once per hour. The job must start quickly, scale automatically, and the engineer should not have to manage a persistent cluster. Which Azure Databricks compute type should the engineer use?
Select an answer first
19application · medium
A machine learning team needs to train models using distributed training with GPU acceleration. They plan to use an interactive cluster for experimentation. Which compute configuration should they choose?
Select an answer first
20expert · hard
A company runs a mix of scheduled ETL jobs and interactive ad-hoc analysis. They want to minimize costs while ensuring that interactive analysis is not impacted by job workloads. They also want to avoid managing multiple clusters. Which approach should they take?
Select an answer first

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Select and Configure Compute in a Workspace DP-750 Practice Questions (Page 4)

Questions 16–20

A data engineer is configuring a classic compute cluster for a workload that has unpredictable demand. The engineer wants the cluster to automatically add and remove workers based on the current workload. Which configuration setting should the engineer enable?

A data engineer needs to run a scheduled production batch job that processes data once per hour. The job must start quickly, scale automatically, and the engineer should not have to manage a persistent cluster. Which Azure Databricks compute type should the engineer use?

A machine learning team needs to train models using distributed training with GPU acceleration. They plan to use an interactive cluster for experimentation. Which compute configuration should they choose?

A company runs a mix of scheduled ETL jobs and interactive ad-hoc analysis. They want to minimize costs while ensuring that interactive analysis is not impacted by job workloads. They also want to avoid managing multiple clusters. Which approach should they take?

Keep studying — Set up and configure an Azure Databricks environment