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Databricks Databricks-Certified-Data-Engineer-Professional Exam Syllabus Topics:

Databricks Certified Data Engineer Professional Sample Questions:

1. A data engineer is working in an interactive notebook with many transformations before outputting the result from display(df.collect() ). The notebook includes wide transformations and a cross join.
The data engineer is getting the following error: "The spark driver has stopped unexpectedly and is restarting. Your notebook will be automatically reattached." Which action should the data engineer take?

A) Rewrite their code to avoid putting memory pressure on the driver node.
B) Look at the compute metrics UI to see if the executors have higher than 90% memory utilization.
C) Run the notebook on a single node cluster to keep driver from falling.
D) Check into the Spark UI to see how many jobs are assigned to each stage as they are employing fewer executors.

2. A data engineer is designing an append-only pipeline that needs to handle both batch and streaming data in Delta Lake. The team wants to ensure that the streaming component can efficiently track which data has already been processed. Which configuration should be set to enable this?

A) overwriteSchema
B) partitionBy
C) mergeSchema
D) checkpointLocation

3. The DevOps team has configured a production workload as a collection of notebooks scheduled to run daily using the Jobs UI. A new data engineering hire is onboarding to the team and has requested access to one of these notebooks to review the production logic.
What are the maximum notebook permissions that can be granted to the user without allowing accidental changes to production code or data?

A) Can Edit
B) Can Manage
C) Can Run
D) No permissions
E) Can Read

4. The following table consists of items found in user carts within an e-commerce website.

The following MERGE statement is used to update this table using an updates view, with schema evolution enabled on this table.

How would the following update be handled?

A) The new nested field is added to the target schema, and files underlying existing records are updated to include NULL values for the new field.
B) The update throws an error because changes to existing columns in the target schema are not supported.
C) The new restored field is added to the target schema, and dynamically read as NULL for existing unmatched records.
D) The update is moved to separate ''restored'' column because it is missing a column expected in the target schema.

5. A data engineer is optimizing a managed Delta table that suffers from data skew and frequently changing query filter columns. The engineer wants to avoid costly data rewrites when query patterns evolve. The table size is under 1 TB. How should the data engineer meet this requirement?

A) Apply Z-ordering, since it allows flexible reorganization of data layout without rewriting existing files and adapts easily to new filter columns.
B) Enable liquid clustering, as it efficiently handles data skew, allows clustering keys to be changed without rewriting existing data, and adapts to evolving query patterns.
C) Combine partitioning and Z-ordering to maximize flexibility and minimize maintenance as query patterns change.
D) Use Hive-style partitioning, as it provides efficient data skipping and is easy to change partition columns at any time.

Solutions: