Databricks Data Engineer Professional: Practice Tests

This course contains the use of artificial intelligence.

A pipeline can finish successfully and still produce the wrong history, expose sensitive data, or replay an external write. Professional-level preparation means recognizing which detail changes the correct engineering decision. These practice tests ask you to reason about data semantics, recovery boundaries, access rules and performance evidence rather than memorize a list of features.

Prepare for the exam introduced on October 9, 2026. Practice across its nine domains, including Lakeflow pipelines and Jobs, Python and SQL, ingestion, VARIANT, AI enrichment, monitoring, optimization, Unity Catalog security and governance, deployment, and data modeling.

Six tests, 420 questions

• Code, Streaming and Production Pipelines: 75 focused questions.
• Ingestion, CDC and Data Manipulation: 75 focused questions.
• Observability and Performance Decisions: 75 focused questions.
• Security, Governance, Deployment and Modeling: 75 focused questions.
• Mock Exam 1: 60 questions in 120 minutes.
• Mock Exam 2: 60 different questions in 120 minutes.

The focused tests give you room to study a topic, with a suggested 150-minute training time. The two mocks approximate the official domain weights with whole-question allocations. They model the 60 scored questions; the real exam may also include up to 10 unscored questions within its 120-minute limit. The course's 80% threshold is a revision goal, not an official passing score.

Learn from the choices you almost selected. Every question includes a correct answer, an overall explanation and feedback for each option. Scenarios distinguish nearby choices: Type 1 versus Type 2 CDC, a materialized view versus a streaming table, a checkpoint versus sink idempotency, a mask versus physical removal, and a configuration deployment versus a successful job run. Python and SQL examples connect these decisions to implementation details.

Use the tests in three passes. First, work through the focused tests and explain your answer before opening the feedback. Second, revisit the missed decisions and check the linked documentation for the rule and its limits. Third, take both mocks under time pressure. Compare the changed conditions across questions and keep a short error log: misunderstood requirement, incorrect API assumption, or missed recovery boundary.

This course is for engineers who already understand Spark, Delta Lake and Databricks fundamentals. It is an independent practice resource, not an official Databricks course or a collection of actual exam questions. Exam content and product capabilities can change; scenarios state relevant version or feature assumptions where they affect the answer.