Databricks Data Engineer Associate: Applied Practice Tests

Categories: Databricks
Share

About Course

This course contains the use of artificial intelligence.

A file has arrived, but the records may still need deduplication. A job is scheduled, but its required data may not be ready. A new commit exists, but it did not produce the stored table version. Prepare for Databricks Certified Data Engineer Associate by tracing these boundaries through a data pipeline.

Practice decisions across the engineering workflow

Work through ingestion and Auto Loader, SQL and PySpark transformations, modeling and quality, Lakeflow Jobs, CI/CD, monitoring and Unity Catalog. Questions ask you to read code, predict results, diagnose dependencies and select a configuration that meets the supplied constraints.

The 390 original questions include four focused tests and two 45-question, 90-minute mocks, following the May 4, 2026 exam-guide scope. The focused banks develop platform and ingestion, transformations, orchestration and deployment, and diagnosis and governance.

Separate a related feature from the required guarantee

Every question includes feedback for every option, an overall explanation and a study tip. Distinguish Delta's transaction contract from Unity Catalog authorization, or the source revision available now from the code that produced existing data. Use these near-miss choices to find the condition you overlooked.

SQL and Python foundations plus some Databricks notebook, table and job experience are recommended. No cloud account is required for the tests; a lab environment is not supplied.

The mocks model the published scored-item count, with close integer domain allocations. They do not imitate unidentified unscored items, and the 80% revision target is not an official passing score. This is independent practice material without Databricks endorsement.

Show More

TRY BEFORE YOU CHOOSE

Three questions. A clearer next step.

Explore original questions and explanations. No account or payment required.

What Will You Learn?

  • Select Databricks compute and ingestion methods using workload, source, cadence, and governance constraints.
  • Distinguish COPY INTO file tracking, Auto Loader schema evolution, checkpoints, and record-level deduplication.
  • Reason through SQL and PySpark transformations, joins, array expansion, aggregation, and data quality rules.
  • Configure Lakeflow Jobs dependencies, retry policies, conditional tasks, loops, and data-driven triggers.
  • Separate Git development, bundle validation, environment promotion, and per-run job parameters.
  • Diagnose performance and execution failures from job history, task graphs, and Spark stage evidence.
  • Apply Unity Catalog table lifecycle, hierarchy privileges, row filters, column masks, and ABAC boundaries.
  • Practice pacing with two 45-question, 90-minute mock exams covering the seven current domains.

Course Content

Practice tests

  • Platform and Ingestion Practice
  • Transformation and Modeling Practice
  • Lakeflow Jobs and CI/CD Practice
  • Monitoring and Governance Practice
  • Timed Mock Exam 1
  • Timed Mock Exam 2