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

Databricks Certified Data Engineer Professional Sample Questions:

Question 1

Assuming that the Databricks CLI has been installed and configured correctly, which Databricks CLI command can be used to upload a custom Python Wheel to object storage mounted with the DBFS for use with a production job?

A. libraries
B. fs
C. workspace
D. jobs
E. configure

Question 2

A data engineer is attempting to execute the following PySpark code:
df = spark.read.table("sales")
result = df.groupBy("region").agg(sum("revenue"))
However, upon inspecting the execution plan and profiling the Spark job, they observe excessive data shuffling during the aggregation phase.
Which technique should be applied to reduce shuffling during the groupBy aggregation operation?

A. Use broadcast join.
B. Repartition by region before aggregation.
C. Use coalesce() after the aggregation.
D. Caching the DataFrame df.

Question 3

A data engineer is using Lakeflow Declarative Pipelines Expectations feature to track the data quality of their incoming sensor data. Periodically, sensors send bad readings that are out of range, and they are currently flagging those rows with a warning and writing them to the silver table along with the good data. They've been given a new requirement ?the bad rows need to be quarantined in a separate quarantine table and no longer included in the silver table.
This is the existing code for their silver table:
@dlt.table
@dlt.expect("valid_sensor_reading", "reading < 120")
def silver_sensor_readings():
return spark.readStream.table("bronze_sensor_readings")
What code will satisfy the requirements?

A. @dlt.table
@dlt.expect_or_drop("valid_sensor_reading", "reading < 120")
def silver_sensor_readings():
return spark.readStream.table("bronze_sensor_readings")
@dlt.table
@dlt.expect_or_drop("invalid_sensor_reading", "reading >= 120")
def quarantine_sensor_readings():
return spark.readStream.table("bronze_sensor_readings")
B. @dlt.table
@dlt.expect_or_drop("valid_sensor_reading", "reading < 120")
def silver_sensor_readings():
return spark.readStream.table("bronze_sensor_readings")
@dlt.table
@dlt.expect("invalid_sensor_reading", "reading >= 120")
def quarantine_sensor_readings():
return spark.readStream.table("bronze_sensor_readings")
C. @dlt.table
@dlt.expect("valid_sensor_reading", "reading < 120")
def silver_sensor_readings():
return spark.readStream.table("bronze_sensor_readings")
@dlt.table
@dlt.expect("invalid_sensor_reading", "reading >= 120")
def quarantine_sensor_readings():
return spark.readStream.table("bronze_sensor_readings")
D. @dlt.table
@dlt.expect_or_drop("valid_sensor_reading", "reading < 120")
def silver_sensor_readings():
return spark.readStream.table("bronze_sensor_readings")
@dlt.table
@dlt.expect("invalid_sensor_reading", "reading < 120")
def quarantine_sensor_readings():
return spark.readStream.table("bronze_sensor_readings")

Question 4

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. Look at the compute metrics UI to see if the executors have higher than 90% memory utilization.
C. Check into the Spark UI to see how many jobs are assigned to each stage as they are employing fewer executors.
D. Rewrite their code to avoid putting memory pressure on the driver node.

Question 5

A developer has successfully configured their credentials for Databricks Repos and cloned a remote Git repository. They do not have privileges to make changes to the main branch, which is the only branch currently visible in their workspace. Which approach allows this user to share their code updates without the risk of overwriting the work of their teammates?

A. Use Repos to pull changes from the remote Git repository; commit and push changes to a branch that appeared as changes were pulled.
B. Use repos to merge all difference and make a pull request back to the remote repository.
C. Use repos to create a fork of the remote repository commit all changes and make a pull request on the source repository
D. Use Repos to create a new branch commit all changes and push changes to the remote Git repertory.
E. Use Repos to merge all differences and make a pull request back to the remote repository.

Solutions: