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Databricks Certified Data Engineer Professional Sample Questions:

1. A data engineer, while designing a Pandas UDF to process financial time-series data with complex calculations that require maintaining state across rows within each stock symbol group, must ensure the function is efficient and scalable. Which approach will solve the problem with minimum overhead while preserving data integrity?

A) Use a SCALAR Pandas UDF that processes the entire dataset at once, implementing custom partitioning logic within the UDF to group by stock symbol and maintain state using global variables shared across all executor processes.
B) Use a grouped_agg Pandas UDF that processes each stock symbol group independently, maintaining state through intermediate aggregation results that get passed between successive UDF calls via broadcast variables.
C) Use a SCALAR_ITER Pandas UDF with iterator-based processing, implementing state management through persistent storage (Delta tables) that gets updated after each batch to maintain continuity across iterator chunks.
D) Use applyInPandas() on a Spark DataFrame that receives all rows for each stock symbol as a Pandas DataFrame, allowing processing within each group while maintaining state variables local to each group's processing function.

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

3. A data engineer is developing a Lakeflow Declarative Pipeline (LDP) using a Databricks notebook directly connected to their pipeline. After adding new table definitions and transformation logic in their notebook, they want to check for any syntax errors in the pipeline code without actually processing data or running the pipeline. How should the data engineer perform this syntax check?

A) Use the "Validate" option in the notebook to check for syntax errors.
B) Switch to a workspace file instead of a notebook to access validation and diagnostics tools.
C) Disconnect the notebook from the pipeline and reconnect it to a compute cluster to access code validation features.
D) Open the web terminal from the notebook and run a shell command to validate the pipeline code.

4. A production workload incrementally applies updates from an external Change Data Capture feed to a Delta Lake table as an always-on Structured Stream job. When data was initially migrated for this table, OPTIMIZE was executed and most data files were resized to 1 GB. Auto Optimize and Auto Compaction were both turned on for the streaming production job. Recent review of data files shows that most data files are under 64 MB, although each partition in the table contains at least 1 GB of data and the total table size is over 10 TB.
Which of the following likely explains these smaller file sizes?

A) Databricks has autotuned to a smaller target file size based on the amount of data in each partition
B) Databricks has autotuned to a smaller target file size based on the overall size of data in the table
C) Databricks has autotuned to a smaller target file size to reduce duration of MERGE operations
D) Z-order indices calculated on the table are preventing file compaction C Bloom filler indices calculated on the table are preventing file compaction

5. A data engineer needs to install the PyYAML Python package within an air-gapped Databricks environment. The workspace has no direct internet access to PyPI. The engineer has downloaded the .whl file locally and wants it available automatically on all new clusters. Which approach should the data engineer use?

A) Set up a private PyPI repository and install via pip index URL.
B) Add the .whl file to Databricks Git Repos and assume automatic installation.
C) Upload the PyYAML .whl file to the user home directory and create a cluster-scoped init script to install it.
D) Upload the PyYAML .whl file to a Unity Catalog Volume, ensure it's allow-listed, and create a cluster-scoped init script that installs it from that path.

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