Free Databricks DEA mock exams

Almost every question on this exam is a short scenario — a pipeline that is slow, a table that needs governing, a team that has to ship the same code to three environments — followed by one plain question. There are no capitalised BEST or MOST words to lean on: the requirements written into the scenario are what rule options out. When a mock answer looks reasonable but is marked wrong, reread the scenario for the constraint you skipped, such as cost, latency, who must see the data, or "without changing the code".

The wrong options are real Databricks features, not invented ones. COPY INTO or Auto Loader, a materialized view or a streaming table, a job cluster or serverless compute, a file arrival trigger or a schedule — each is the right tool somewhere, so knowing what a feature does is not enough. Know when it is the wrong choice.

Product names have moved recently, and the exam uses the current ones: Lakeflow Jobs, Lakeflow Connect, Lakeflow Spark Declarative Pipelines, Declarative Automation Bundles (formerly Databricks Asset Bundles) and Git Folders (formerly Repos). If you learned the platform under the older names, map them before mock day; a miss here is often a right idea under a name you did not recognise.

Ingestion and transformation carry the most weight between them, and both expect you to read PySpark and SQL: a join, an explode, a dedup, an aggregation. You will not write code in the exam, but you must predict what a snippet does. If your misses cluster on code questions, practise by reading short snippets and saying the output aloud before you look at the options.

Troubleshooting and governance questions reward people who have opened the Spark UI and granted privileges in Unity Catalog. Skew, shuffle and spill are told apart by stage and task metrics, and access questions turn on where in the catalog–schema–table hierarchy a privilege is granted and what it inherits. Ten minutes in a real workspace fixes more of these than another read of the documentation.