A company runs a data pipeline that consists of multiple jobs with varying resource requirements. Some jobs are short and frequent, others are long and infrequent. The team wants to optimize costs while maintaining performance. Which approach is most cost-effective?

Use separate job clusters for each job, with autoscaling enabled

Use a single all-purpose cluster for all jobs

Use SQL Warehouses for all jobs

Use a single job cluster for all jobs, with a fixed size

A team needs to run a workload that requires GPU acceleration for deep learning. They are evaluating compute options. Which compute service should they use?

Classic Compute with CPU instances

Classic Compute with GPU instances

Which characteristic is true for Serverless Compute in Databricks?

It requires you to manually configure and manage cluster sizes.

It is only available for SQL workloads, not for notebooks or jobs.

It provides automatic scaling and infrastructure management by Databricks.

It requires you to specify a fixed cluster size that cannot change.

DatabricksCertified Data Engineer Associate

Domain 1Objective 2

Understand Databricks Data Intelligence Platform’s Compute Services, Including Their Characteristics, Limitations, and Cost Models, and Select the Most Suitable Option for Each Workload Use Case. DATA-ENGINEER-ASSOCIATE Practice Questions (Page 1)

Part of the Databricks Intelligence Platform domain, which accounts for 6% of the DATA-ENGINEER-ASSOCIATE exam. Databricks does not publish an official question count, but from its 90-minute exam (~35–60 total, ~2–4 in this domain), expect 1–2 from this objective — we provide 15 practice questions to prepare you well beyond it. (estimate)

15questions here

3free pages

5concepts

6%of the exam

Questions 1–5

1expert · hard
A company runs a data pipeline that consists of multiple jobs with varying resource requirements. Some jobs are short and frequent, others are long and infrequent. The team wants to optimize costs while maintaining performance. Which approach is most cost-effective?
Select an answer first
2application · medium
A team needs to run a workload that requires GPU acceleration for deep learning. They are evaluating compute options. Which compute service should they use?
Select an answer first
3foundation · easy
Which compute service in Databricks has a limitation that it is not suitable for running arbitrary Python or Scala code in notebooks?
Select an answer first
4foundation · easy
A business intelligence team needs to run interactive SQL queries on a data warehouse for dashboards. They require consistent performance and do not need to run Python or Scala code. Which compute service is the most suitable?
Select an answer first
5foundation · easy
Which characteristic is true for Serverless Compute in Databricks?
Select an answer first

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Understand Databricks Data Intelligence Platform’s Compute Services, Including Their Characteristics, Limitations, and Cost Models, and Select the Most Suitable Option for Each Workload Use Case. DATA-ENGINEER-ASSOCIATE Practice Questions (Page 1)

Questions 1–5

A company runs a data pipeline that consists of multiple jobs with varying resource requirements. Some jobs are short and frequent, others are long and infrequent. The team wants to optimize costs while maintaining performance. Which approach is most cost-effective?

A team needs to run a workload that requires GPU acceleration for deep learning. They are evaluating compute options. Which compute service should they use?

Which compute service in Databricks has a limitation that it is not suitable for running arbitrary Python or Scala code in notebooks?

A business intelligence team needs to run interactive SQL queries on a data warehouse for dashboards. They require consistent performance and do not need to run Python or Scala code. Which compute service is the most suitable?

Which characteristic is true for Serverless Compute in Databricks?

Keep studying — Databricks Intelligence Platform