A company has a mix of workloads: a few long-running batch jobs that run nightly, a team of analysts who run ad-hoc SQL queries during business hours, and a machine learning team that needs GPU instances for training. The company wants to minimize costs and management overhead. Which compute strategy should they adopt?

Use job clusters for batch jobs, a SQL warehouse for ad-hoc queries, and a separate GPU cluster for ML training.

Use a single large cluster for all workloads, with autoscaling and a long termination time.

Use a pool of instances for all workloads to reduce startup time.

Use serverless compute for all workloads.

A company runs a nightly batch job that performs heavy SQL aggregations on a large Delta table. The job currently takes 45 minutes. They want to reduce runtime without changing the code. Which compute feature should they enable?

Increase the number of worker nodes.

Enable Photon acceleration on the cluster.

Switch to a serverless compute.

A data science team uses an interactive notebook to explore a large dataset. They need to install a custom Python library that is not available in the default Databricks runtime. The library must be available every time they start a new cluster. Which approach should they take?

Create a new cluster and manually install the library each time.

Install the library using %pip in the notebook and attach the cluster.

Use a job compute cluster and install the library in the job task.

Add the library as a cluster library and install it on the cluster.

Microsoft Certified:Azure Databricks Data Engineer Associate

Domain 1Objective 1

Select and Configure Compute in a Workspace DP-750 Practice Questions (Page 1)

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 1–5

1expert · hard
A company has a mix of workloads: a few long-running batch jobs that run nightly, a team of analysts who run ad-hoc SQL queries during business hours, and a machine learning team that needs GPU instances for training. The company wants to minimize costs and management overhead. Which compute strategy should they adopt?
Select an answer first
2application · medium
A company runs a nightly batch job that performs heavy SQL aggregations on a large Delta table. The job currently takes 45 minutes. They want to reduce runtime without changing the code. Which compute feature should they enable?
Select an answer first
3foundation · easy
A data engineer wants to install a library from a private PyPI repository onto a cluster. Which library source should the engineer select when installing the library?
Select an answer first
4foundation · easy
A data engineer wants to improve the performance of SQL queries on a cluster by enabling a vectorized query engine that is built into Azure Databricks. Which feature should the engineer enable in the cluster configuration?
Select an answer first
5application · medium
A data science team uses an interactive notebook to explore a large dataset. They need to install a custom Python library that is not available in the default Databricks runtime. The library must be available every time they start a new cluster. 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 1)

Questions 1–5

A company runs a nightly batch job that performs heavy SQL aggregations on a large Delta table. The job currently takes 45 minutes. They want to reduce runtime without changing the code. Which compute feature should they enable?

A data engineer wants to install a library from a private PyPI repository onto a cluster. Which library source should the engineer select when installing the library?

A data engineer wants to improve the performance of SQL queries on a cluster by enabling a vectorized query engine that is built into Azure Databricks. Which feature should the engineer enable in the cluster configuration?

A data science team uses an interactive notebook to explore a large dataset. They need to install a custom Python library that is not available in the default Databricks runtime. The library must be available every time they start a new cluster. Which approach should they take?

Keep studying — Set up and configure an Azure Databricks environment