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Databricks Certified Data Engineer Professional Sample Questions:

1. 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) 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.
B) Download worker logs directly from the Spark UI and ignore driver logs, as worker logs contain stdout/stderr for all tasks and cluster events.
C) Use the notebook interactive debugger to re-run the entire multi-task job, and capture step- through traces for the failing task.
D) Export the notebook run results to HTML; this bundle includes complete stdout, stderr, and cluster event history across all tasks.

2. The following code has been migrated to a Databricks notebook from a legacy workload:

The code executes successfully and provides the logically correct results, however, it takes over
20 minutes to extract and load around 1 GB of data.
Which statement is a possible explanation for this behavior?

A) %sh triggers a cluster restart to collect and install Git. Most of the latency is related to cluster startup time.
B) Python will always execute slower than Scala on Databricks. The run.py script should be refactored to Scala.
C) %sh executes shell code on the driver node. The code does not take advantage of the worker nodes or Databricks optimized Spark.
D) Instead of cloning, the code should use %sh pip install so that the Python code can get executed in parallel across all nodes in a cluster.
E) %sh does not distribute file moving operations; the final line of code should be updated to use %fs instead.

3. A task orchestrator has been configured to run two hourly tasks. First, an outside system writes Parquet data to a directory mounted at /mnt/raw_orders/. After this data is written, a Databricks job containing the following code is executed:

Assume that the fields customer_id and order_id serve as a composite key to uniquely identify each order, and that the time field indicates when the record was queued in the source system.
If the upstream system is known to occasionally enqueue duplicate entries for a single order hours apart, which statement is correct?

A) Duplicate records arriving more than 2 hours apart will be dropped, but duplicates that arrive in the same batch may both be written to the orders table.
B) All records will be held in the state store for 2 hours before being deduplicated and committed to the orders table.
C) Duplicate records enqueued more than 2 hours apart may be retained and the orders table may contain duplicate records with the same customer_id and order_id.
D) The orders table will contain only the most recent 2 hours of records and no duplicates will be present.
E) The orders table will not contain duplicates, but records arriving more than 2 hours late will be ignored and missing from the table.

4. A data engineer is building a streaming data pipeline to ingest JSON files from cloud storage into a Delta Lake table. The pipeline must process files incrementally, handle schema evolution automatically, ensure exactly-once processing, and minimize manual infrastructure management.
How should the data engineer fulfill these requirements?

A) Use Lakeflow Spart Declarative Pipelines with Auto Loader and enabling schema inference with
"cloudFiles.schemaEvolutionMode"= "addNewColumns"
B) Use Lakeflow Spark Declarative Pipelines with a static DataFrame read, merge schema with spark.conf.set ("spark.databricks.delta.schema.autoMerge.enabled", "true")
C) Use traditional Spark Structured Streaming with Auto Loader, manually configuring checkpoints location and enabling schema inference with "mergeSchema"= "true"
D) Use Auto Loader in batch mode with a daily job to overwrite the Delta table.

5. Which statement describes a key benefit of an end-to-end test?

A) It provides testing coverage for all code paths and branches.
B) It makes it easier to automate your test suite
C) It pinpoint errors in the building blocks of your application.
D) It closely simulates real world usage of your application.

Solutions: