Databricks DE-PRO Sample Questions with Explanations

These original sample questions are designed to help you check how the exam topics appear in decision-style prompts. They are not taken from the live exam.

Use these sample questions as a guided self-assessment for Databricks Data Engineer Professional (DE-PRO) topics such as production pipelines, Lakeflow, streaming, system tables, event logs, Unity Catalog, Asset Bundles, performance evidence, and recoverable deployments.

Where these questions fit in the DE-PRO guide

The sample set below is part of the Databricks DE-PRO guide path:

• Databricks certification hub
• DE-PRO exam guide
• DE-PRO cheat sheet
• DE-PRO study plan
• DE-PRO FAQ
• DE-PRO resources
• DE-PRO glossary

DE-PRO production data engineering sample questions

Work through each prompt before opening the explanation. DE-PRO questions usually reward observable, repeatable, recoverable pipeline design over notebook-only fixes.

Question 1

Topic: Recoverable streaming pipeline

A streaming pipeline failed after a bad upstream schema change. The team needs to recover without losing checkpoint integrity or silently skipping malformed records. Which approach is strongest?

• A. Delete the checkpoint and restart from the latest data without recording the gap.
• B. Inspect event logs and failed records, repair or quarantine the bad data, preserve checkpoint semantics, and rerun through a controlled recovery path.
• C. Disable schema validation permanently so all future records pass.
• D. Manually edit output tables until dashboards look correct.

Best answer: B

Explanation: Professional data-engineering answers protect recoverability, observability, and correctness. Checkpoints, event logs, quarantine patterns, and controlled repair preserve operational trust.

Why the other choices are weaker:

• A can hide data loss or duplication.
• C weakens data quality permanently.
• D is not repeatable or auditable.

What this tests: streaming recovery, checkpoints, event logs, quarantine, schema handling, and pipeline reliability.

Related topics: Streaming; Checkpoints; Event logs; Recovery

Question 2

Topic: Deploying pipelines across environments

A team wants to promote a data pipeline from development to staging and production with reviewed configuration, repeatable resource definitions, and fewer manual notebook edits. Which Databricks pattern best fits?

• A. Use Databricks Asset Bundles or an equivalent CI/CD deployment pattern for versioned resources and environment-specific configuration.
• B. Ask one engineer to copy notebook cells into production by hand.
• C. Store secrets in markdown comments because they are easy to find.
• D. Disable review because production edits should be fast.

Best answer: A

Explanation: DE-PRO tests deployment discipline. Bundles and CI/CD patterns make promotion repeatable, reviewable, parameterized, and easier to roll back.

Why the other choices are weaker:

• B is error-prone and not auditable enough.
• C exposes secrets.
• D weakens production change control.

What this tests: Asset Bundles, CI/CD, environment promotion, configuration, and deployment governance.

Related topics: Asset Bundles; CI/CD; Deployment; Environments

Question 3

Topic: Performance evidence before resizing

A daily transformation became slower and more expensive after a new join. What should the engineer inspect before increasing compute?

• A. Spark UI, query profile or execution metrics, shuffle volume, skew, file layout, and pruning evidence.
• B. The dashboard title because titles affect Spark joins.
• C. Only the number of users in the workspace.
• D. Whether the team can ignore the SLA until the next release.

Best answer: A

Explanation: Professional performance work starts from evidence. The new join may have introduced skew, shuffle, bad pruning, or layout issues that compute alone will not fix cleanly.

Why the other choices are weaker:

• B is unrelated to execution behavior.
• C is too broad and ignores workload evidence.
• D avoids operational responsibility.

What this tests: Spark UI, shuffle, skew, pruning, layout, query profile, and cost-aware tuning.

Related topics: Spark UI; Performance; Shuffle; Skew

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