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Databricks Certified Data Engineer Professional Sample Questions:

1. A job runs four independent tasks (X, Y, Z, W) in parallel to process regional sales data. The Data Engineering team recently updated its cluster policy to ban cost-prohibitive instance types. Task Y now fails due to the newly enforced cluster policy restricting the use of a specific instance type.
A data engineer needs to resolve the failure quickly without disrupting the other tasks. How should the data engineer resolve the failure of tasks?

A) Use "Repair run", override the cluster configuration for Task Y to use a permitted instance type, and let Databricks re-run only Task Y.
B) Edit the global cluster policy to allow the restricted instance type, then re-run the entire job.
C) Delete the failed run, disable the cluster policy, and re-execute all tasks.
D) Manually create a new cluster for Task Y, update the job configuration, and trigger a full re-run.

2. A data architect is designing a Databricks solution to efficiently process data for different business requirements. In which scenario should a data engineer use a materialized view compared to a streaming table?

A) Precomputing complex aggregations and joins from multiple large tables to accelerate BI dashboard performance.
B) Ingesting data from Apache Kafka topics with sub-second processing requirements for immediate alerting.
C) Processing high-volume, continuous clickstream data from a website to monitor user behavior in real-time.
D) Implementing a CDC (Change Data Capture) pipeline that needs to detect and respond to database changes within seconds.

3. A data engineer deploys a multi-task Databricks job that orchestrates three notebooks. One task intermittently fails with Exit Code 1 but succeeds on retry. The engineer needs to collect detailed logs for the failing attempts, including stdout/stderr and cluster lifecycle context, and share them with the platform team. What steps the data engineer needs to follow using built-in tools?

A) Download worker logs directly from the Spark UI and ignore driver logs, as worker logs contain stdout/stderr for all tasks and cluster events.
B) Export the notebook run results to HTML; this bundle includes complete stdout, stderr, and cluster event history across all tasks.
C) Use the notebook interactive debugger to re-run the entire multi-task job, and capture step- through traces for the failing task.
D) From the job run details page, export the job's logs or configure log delivery; then retrieve the compute driver logs and event logs from the compute details page to correlate stdout/stderr with cluster events.

4. A data engineer inherits a Delta table with historical partitions by country that are badly skewed.
Queries often filter by high-cardinality customer_id and vary across dimensions over time. The engineer wants a strategy that avoids a disruptive full rewrite, reduces sensitivity to skewed partitions, and sustains strong query performance as access patterns evolve. Which two actions should the data engineer take? (Choose two.)

A) Depend solely on optimized writes; Databricks will automatically replace partitioning with clustering over time.
B) Keep existing partitions and rely on bin-packing OPTIMIZE only; ZORDER and clustering are unnecessary for multi-dimensional filters.
C) Periodically run OPTIMIZE table_name.
D) Switch from static partitioning to liquid clustering and select initial clustering keys that reflect common filters such as customer_id.
E) Disable data skipping statistics to avoid maintenance overhead; rely on adaptive query execution instead.

5. Which configuration parameter directly affects the size of a spark-partition upon ingestion of data into Spark?

A) spark.sql.files.maxPartitionBytes
B) spark.sql.autoBroadcastJoinThreshold
C) spark.sql.files.openCostInBytes
D) spark.sql.adaptive.advisoryPartitionSizeInBytes
E) spark.sql.adaptive.coalescePartitions.minPartitionNum

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