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

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Databricks Certified Data Engineer Professional Sample Questions:

1. A data engineer is analyzing a large, partitioned retail dataset in Databricks, where each row represents a sale made by a salesperson. The dataset contains millions of records with the following schema:
sales_df: [salesperson_id: string, region: string, sale_amount: double, sale_date: date] The data engineer needs to generate a DataFrame that ranks salespeople within each region based on their total cumulative sales, with the highest seller ranked as 1. If multiple salespeople have the same total sales, they should share the same rank.
The data engineer wants to implement this logic using a PySpark window function and the dense_rank () function.
Which code snippet will perform this ranking?

A)

B)

C)

D)

2. 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) Disable data skipping statistics to avoid maintenance overhead; rely on adaptive query execution instead.
C) Switch from static partitioning to liquid clustering and select initial clustering keys that reflect common filters such as customer_id.
D) Periodically run OPTIMIZE table_name.
E) Keep existing partitions and rely on bin-packing OPTIMIZE only; ZORDER and clustering are unnecessary for multi-dimensional filters.

3. A data engineer needs to capture pipeline settings from an existing in the workspace, and use them to create and version a JSON file to create a new pipeline. Which command should the data engineer enter in a web terminal configured with the Databricks CLI?

A) Use list pipelines to get the specs for all pipelines; get the pipeline spec from the return results parse and use this to create a pipeline
B) Stop the existing pipeline; use the returned settings in a reset command
C) Use the alone command to create a copy of an existing pipeline; use the get JSON command to get the pipeline definition; save this to git
D) Use the get command to capture the settings for the existing pipeline; remove the pipeline_id and rename the pipeline; use this in a create command

4. A data engineer is configuring Delta Sharing for a Databricks-to-Databricks scenario to optimize read performance. The recipient needs to perform time travel queries and streaming reads on shared sales data. Which configuration will provide the optimal performance while enabling these capabilities?

A) Share the entire schema WITHOUT HISTORY and rely on recipient-side caching for performance.
B) Share tables WITHOUT HISTORY and enable partitioning for better query performance.
C) Share tables WITH HISTORY, ensure tables don't have partitioning enabled, and enable CDF before sharing.
D) Use the open sharing protocol instead of Databricks-to-Databricks sharing for better performance.

5. Which distribution does Databricks support for installing custom Python code packages?

A) nom
B) Wheels
C) CRAN
D) CRAM
E) sbt
F) jars

Solutions: