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Databricks Certified Data Engineer Professional Sample Questions:

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) fs
B) jobs
C) workspace
D) libraries
E) configure

2. A user wants to use DLT expectations to validate that a derived table report contains all records from the source, included in the table validation_copy.
The user attempts and fails to accomplish this by adding an expectation to the report table definition.

Which approach would allow using DLT expectations to validate all expected records are present in this table?

A) Define a temporary table that perform a left outer join on validation_copy and report, and define an expectation that no report key values are null
B) Define a view that performs a left outer join on validation_copy and report, and reference this view in DLT expectations for the report table
C) Define a function that performs a left outer join on validation_copy and report and report, and check against the result in a DLT expectation for the report table
D) Define a SQL UDF that performs a left outer join on two tables, and check if this returns null values for report key values in a DLT expectation for the report table.

3. A data engineer is troubleshooting a slow-running Delta Lake query on Databricks SQL involves complex joins and large datasets. They need to identify whether the root cause is related to poor data skipping, inefficient join strategies, or excessive data shuffling. Which approach should identify the specific bottlenecks using native Databricks tools?

A) Enable the EXPLAIN command to review the parsed logical plan and manually estimate shuffle sizes.
B) Use the LIMIT clause to run a subset of the query and compare execution times with the full dataset.
C) Analyze the Top Operators panel in the Query Profile to identify high-cost operations like BroadcastNestedLoopJoin
D) Check the query's execution time in the Jobs UI and correlate it with cluster resource utilization metrics.

4. A junior data engineer has been asked to develop a streaming data pipeline with a grouped aggregation using DataFrame df. The pipeline needs to calculate the average humidity and average temperature for each non-overlapping five-minute interval. Events are recorded once per minute per device.
Streaming DataFrame df has the following schema:
"device_id INT, event_time TIMESTAMP, temp FLOAT, humidity FLOAT"
Code block:

Choose the response that correctly fills in the blank within the code block to complete this task.

A) to_interval("event_time", "5 minutes").alias("time")
B) window("event_time", "5 minutes").alias("time")
C) lag("event_time", "10 minutes").alias("time")
D) window("event_time", "10 minutes").alias("time")
E) "event_time"

5. A junior data engineer is working to implement logic for a Lakehouse table named silver_device_recordings. The source data contains 100 unique fields in a highly nested JSON structure.
The silver_device_recordings table will be used downstream to power several production monitoring dashboards and a production model. At present, 45 of the 100 fields are being used in at least one of these applications.
The data engineer is trying to determine the best approach for dealing with schema declaration given the highly-nested structure of the data and the numerous fields.
Which of the following accurately presents information about Delta Lake and Databricks that may impact their decision-making process?

A) Human labor in writing code is the largest cost associated with data engineering workloads; as such, automating table declaration logic should be a priority in all migration workloads.
B) The Tungsten encoding used by Databricks is optimized for storing string data; newly-added native support for querying JSON strings means that string types are always most efficient.
C) Because Delta Lake uses Parquet for data storage, data types can be easily evolved by just modifying file footer information in place.
D) Schema inference and evolution on .Databricks ensure that inferred types will always accurately match the data types used by downstream systems.
E) Because Databricks will infer schema using types that allow all observed data to be processed, setting types manually provides greater assurance of data quality enforcement.

Solutions: