Databricks Data Engineer Professional test

Question 1. A streaming job must guarantee that each source record affects the target exactly once, even after a driver restart. What makes this possible?

• A. Increasing the retry count
• B. Checkpointed offsets combined with an idempotent or transactional sink
• C. Writing to a temporary table and renaming it
• D. Disabling automatic restart

Question 2. A Python UDF processes 400 million rows and dominates job runtime. What is the first change to consider?

• A. Replace it with built-in SQL expressions or a vectorised UDF
• B. Double the cluster size
• C. Increase the driver memory only
• D. Cache the input DataFrame

Question 3. A join between a 4 TB fact table and a 30 MB dimension spills heavily. What should be verified first?

• A. Whether the fact table is sorted
• B. Whether the dimension has a primary key constraint
• C. Whether the small side is being broadcast, and why the optimiser chose otherwise
• D. Whether the cluster has enough disk

Question 4. Code must behave identically whether run on a batch backfill or an incremental stream. Which approach supports this best?

• A. Maintain two separate implementations
• B. Run the batch version and copy results into the stream target
• C. Disable the streaming version during backfill
• D. One shared transformation, parameterised by read and write mode

Question 5. A widely reused transformation must be testable outside a notebook. What is the appropriate structure?

• A. Extract it into functions in a version-controlled module with unit tests
• B. Copy the cells into each notebook that needs it
• C. Store the SQL in a table and read it at runtime
• D. Document the steps in a wiki page

Question 6. A source system provides a change data capture feed with inserts, updates and deletes. What must the target logic handle?

• A. Append every event and let consumers work it out
• B. Ordered merge semantics including deletes and late or out-of-order events
• C. Inserts only, ignoring updates and deletes
• D. A full reload each night regardless of the feed

Question 7. A team must keep Delta tables in sync with changes in an operational PostgreSQL database, with as little custom extraction code as possible. Which approach does the exam guide point to?

• A. A Lakeflow Connect CDC ingestion pipeline from the PostgreSQL source
• B. A nightly full export of every table to cloud storage
• C. Lakehouse Federation queries against PostgreSQL from every downstream job
• D. A notebook that polls each table for rows with a newer timestamp

Question 8. Downstream consumers must never see rows that fail validation, but the rows must remain investigable. What design fits?

• A. Drop failing rows silently
• B. Load everything and let consumers filter
• C. Fail the whole job on any invalid row
• D. Route failing rows to a quarantine table and publish only valid rows

Question 9. A quality rule must fail the pipeline when more than 1% of rows are invalid, but tolerate less. What is required?

• A. A row-level constraint that rejects each invalid row
• B. A threshold expectation evaluated across the batch
• C. A manual review after each run
• D. A warning comment in the code

Question 10. A streaming job silently falls behind during peak hours. Which metric most directly reveals this?

• A. Input backlog or source lag over time
• B. Notebook cell execution count
• C. Number of columns written
• D. Workspace user count

Question 11. An alert should fire only when a failure is genuinely actionable, not for transient retried errors. How should it be configured?

• A. Alert on every task error immediately
• B. Alert only once per month in a summary
• C. Alert on final failure after retries are exhausted
• D. Disable alerting and review logs weekly

Question 12. A daily job's cost has tripled while data volume grew 20%. Profiling shows heavy file listing. What is the likely cause?

• A. The cluster is undersized
• B. Small file accumulation in the source or target tables
• C. Too few columns are selected
• D. The job runs too early in the day

Question 13. A table is almost always filtered on two columns with high cardinality. Which physical design helps most?

• A. Partition by both columns
• B. Sort the data alphabetically by the first column only
• C. Cluster or z-order the table on those columns
• D. Convert the table to CSV

Question 14. Interactive development clusters are left running overnight. Which control reduces cost with least disruption?

• A. Auto-termination after a short idle period
• B. Removing cluster creation permissions from engineers
• C. Reducing the number of workers to one permanently
• D. Restricting development to office hours by policy only

Question 15. Personal data must be removable on request, but the table is append-only with historical versions retained. What must be considered?

• A. Nothing; deleting the current row is sufficient
• B. Retained historical versions and downstream copies must also be addressed within the retention policy
• C. The table must be converted to CSV first
• D. Only the dashboard needs updating

Question 16. A job's service principal has workspace administrator rights for convenience. What is the finding?

• A. No issue, as jobs are trusted
• B. It increases cost
• C. It slows the job down
• D. Excessive privilege; the principal should be scoped to only what the job needs

Question 17. Analysts keep building their own copies of customer data because they cannot find the curated table. Which Unity Catalog capability addresses this most directly?

• A. Grant every analyst ALL PRIVILEGES on the catalog
• B. Publish yet another copy with a clearer name
• C. Enforce a naming convention and nothing else
• D. Add comments and tags to the curated table and its columns

Question 18. A job fails in production but succeeds in development with the same code. What should be compared first?

• A. Environment configuration, input data characteristics and permissions
• B. The notebook’s formatting
• C. The names of the developers
• D. The workspace colour theme

Question 19. A deployment must be reversible within minutes if data quality degrades. What supports this?

• A. A detailed runbook with no automation
• B. Keeping the previous notebook open in a browser tab
• C. Versioned deployment plus table versioning and a tested rollback procedure
• D. Deploying only on Fridays

Question 20. Two gold tables and several dashboards each compute monthly revenue differently, and the figures disagree. What modelling remedy fits the current outline?

• A. Document both definitions and let consumers choose
• B. Define the metric once as a Unity Catalog metric view and point consumers at it
• C. Delete one of the tables without consultation
• D. Rename the columns to avoid confusion