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Databricks Certified Data Engineer Professional Sample Questions:

1. A data engineer is implementing Unity Catalog governance for a multi-team environment. Data scientists need interactive clusters for basic data exploration tasks, while automated ETL jobs require dedicated processing. How should the data engineer configure cluster isolation policies to enforce least privilege and ensure Unity Catalog compliance?

A) Configure all clusters with NO ISOLATION_SHARED access mode since Unity Catalog works with any cluster configuration.
B) Create compute policies with STANDARD access mode for interactive workloads and DEDICATED access mode for automated jobs.
C) Allow all users to create any cluster type and rely on manual configuration to enable Unity Catalog access modes.
D) Use only DEDICATED access mode for both interactive workloads and automated jobs to maximize security isolation.

2. The data engineer is using Spark's MEMORY_ONLY storage level. Which indicators should the data engineer look for in the spark UI's Storage tab to signal that a cached table is not performing optimally?

A) The RDD Block Name included the '' annotation signaling failure to cache
B) The number of Cached Partitions> the number of Spark Partitions
C) On Heap Memory Usage is within 75% of off Heap Memory usage
D) Size on Disk is> 0
E) Size on Disk is < Size in Memory

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

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

4. A data engineer is running a groupBy aggregation on a massive user activity log grouped by user_id. A few users have millions of records, causing task skew and long runtimes. Which technique will fix the skew in this aggregation?

A) Use reduceByKey instead of groupBy to avoid shuffles.
B) Filter out the skewed users before the aggregation.
C) Use salting by adding a random prefix to skewed keys before aggregation, then aggregate again after removing the prefix.
D) Increase the Spark driver memory and retry.

5. 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) 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.
C) Export the notebook run results to HTML; this bundle includes complete stdout, stderr, and cluster event history across all tasks.
D) Use the notebook interactive debugger to re-run the entire multi-task job, and capture step- through traces for the failing task.

Solutions: