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Databricks Certified Data Engineer Professional - 340 Questions

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Practice exam for the Databricks Certified Data Engineer Professional certification. Covers Databricks tooling, data processing, data modeling, security and governance, monitoring, and testing and deployment.

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Sample Questions — Databricks Certified Data Engineer Professional - 340 Questions

5 free sample questions from this practice exam. Correct answers are highlighted.

1. A data engineering team uses Databricks Repos to manage their notebooks. A junior engineer accidentally pushes broken code directly to the main branch, causing production pipelines to fail. Which process should the team adopt to prevent this from happening in the future?

A Grant write access to Databricks Repos only to senior engineers and revoke it from junior engineers.
B Enable branch protection on the main branch and require pull request reviews before any merge is allowed. ✓ Correct
C Have all engineers commit to a single shared feature branch and merge to main once per sprint.
D Delete stale branches after each release cycle to keep the repository clean.

2. A Databricks workspace administrator wants to automate the nightly restart of a long-running cluster using a shell script on an external CI/CD server. Which approach is the most appropriate?

A Log in to the Databricks UI every night and manually restart the cluster from the Compute page.
B Schedule a notebook on the cluster itself to call dbutils.cluster.restart() at midnight.
C Use the Databricks CLI on the CI/CD server to call the clusters restart command in a cron job. ✓ Correct
D Connect to the cluster through the JDBC endpoint and issue a SQL RESTART CLUSTER command.

3. A team needs to programmatically create a multi-task Databricks Job that includes a notebook task, a Python wheel task, and a dbt task, all with task dependencies. Which API should they use?

A Jobs API 2.1, using the tasks array with depends_on to define dependencies between tasks. ✓ Correct
B Jobs API 2.0, using the notebook_task field to chain tasks sequentially.
C Clusters API, configuring init scripts that launch tasks in the correct order.
D Pipelines API, defining each task as a separate Delta Live Tables pipeline stage.

4. A Databricks administrator wants to ensure that data scientists cannot provision clusters with more than 10 worker nodes and must use a specific Databricks Runtime version. Which feature enforces these constraints?

A Configure an instance pool with a fixed number of idle instances and attach it to all user clusters.
B Create a cluster policy that defines maximum worker count and pinned Databricks Runtime versions, then assign it to users. ✓ Correct
C Attach an IAM role to the workspace that restricts EC2 instance launch permissions.
D Apply cluster tags that the billing system uses to block clusters exceeding a node threshold.

5. Which statement correctly describes a Databricks Workflow with a conditional task branching?

A Databricks Workflows only support linear task sequences and cannot branch based on task outcomes.
B Run-if conditions in Workflows accept arbitrary Python expressions evaluated at the driver node.
C All tasks in a Databricks Workflow must run on the same job cluster to share SparkContext.
D Databricks Workflows support run-if conditions that execute tasks based on the outcome state of upstream tasks. ✓ Correct

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Questions 340
Time 2h
Difficulty Hard
Minimum Score 70.00%
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