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Databricks Certified Data Engineer Professional Sample Questions:

1. What is the first line of a Databricks Python notebook when viewed in a text editor?

A) # MAGIC %python
B) -- Databricks notebook source
C) # Databricks notebook source
D) %python
E) // Databricks notebook source

2. 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) Stop the existing pipeline; use the returned settings in a reset command
B) 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
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 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

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

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

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

5. A Data engineer wants to run unit's tests using common Python testing frameworks on python functions defined across several Databricks notebooks currently used in production. How can the data engineer run unit tests against function that work with data in production?

A) Run unit tests against non-production data that closely mirrors production
B) Define and unit test functions using Files in Repos
C) Define units test and functions within the same notebook
D) Define and import unit test functions from a separate Databricks notebook

Solutions: