The complete Databricks Certified Data Engineer Associate study library — 13 lessons mapped to every domain and topic in the official exam guide. Free to read, no account needed.
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Databricks publishes exactly what the Databricks DEA tests and how much each part is worth. Here it is — every domain, its weight, the topics inside it, and the lessons that cover them.
The Data Intelligence Platform itself: its architecture, Delta Lake and Unity Catalog, and choosing the right compute for each workload by characteristics, limits and cost.
Getting data in: batch, streaming and incremental patterns, COPY INTO, Auto Loader, Lakeflow Connect connectors, JDBC/ODBC and REST clients, choosing between ingestion methods, and semi-structured data.
Bronze to silver to gold with PySpark and SQL: cleaning, joins and unions, column and row manipulation, deduplication and aggregation, basic Spark tuning, gold-layer objects in Unity Catalog, and data quality checks.
Orchestrating pipelines with Lakeflow Jobs: control flow (retries, branching, looping), task types and dependencies in the DAG, schedules and trigger types, and choosing time-based versus data-driven triggers.
Shipping data code safely: Git Folders branching, commits and pull requests in the workspace, Declarative Automation Bundles (formerly Databricks Asset Bundles) with per-environment variables and overrides, and the Databricks CLI in CI/CD.
Keeping pipelines healthy and fast: run-history trends and job health in the Lakeflow Jobs UI, skew, shuffle and spill in the Spark UI, Liquid Clustering and predictive optimization, and diagnosing startup, library and memory failures.
Unity Catalog governance: managed versus external tables, GRANT/REVOKE/DENY across the securable hierarchy, column masking and row-level security, and ABAC policies for central filtering and masking.
Reading every note front to back is the slowest way to pass. The exam is weighted, so your revision should be too — the blueprint above tells you exactly where the marks are.
Data Transformation and Modeling carries the most marks on the Databricks DEA, so it earns the most of your attention. Work down the blueprint from the heaviest domain to the lightest rather than starting at domain 1 and grinding forward.
Read each lesson once without stopping to memorise — you are building a map, not a memory. On the second pass, read only the "what's tested" and key-takeaway blocks. If a takeaway surprises you, that lesson goes back on the pile.
Cover a lesson's key takeaways and try to say them out loud from the heading alone. Recall you can produce under your own steam is the only recall that survives a timed exam; recognition on a re-read is not the same thing.
Finishing the notes is not the goal — scoring is. Move to Databricks DEA practice questions early, then come back to the exact lessons your wrong answers point at. That loop is worth far more than a third read-through.
Exam structure, domain weights and scoring on this page come from the official Databricks DEA exam guide.