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DATABRICKS CERTIFIED DATA ENGINEER ASSOCIATE PRACTICE
QUESTIONS AND ANSWERS 2026 WITH COMPLETE
DATABRICKS CERTIFIED DATA ENGINEER ASSOCIATE PRACTICE
QUESTIONS AND ANSWERS 2026 WITH COMPLETE
A data analyst has noticed that their Databricks SQL queries are
running too slowly. They claim that this issue is affecting all of
their sequentially run queries. They ask the data engineering
team for help. The data engineering team notices that each of
the queries uses the same SQL endpoint, but the SQL endpoint
is not used by any other user.
Which of the following approaches can the data engineering
team use to improve the latency of the data analyst's queries?
They can increase the cluster size of the SQL endpoint
A data architect is designing a data model that works for both
video-based machine learning workloads and highly audited
batch ETL/ELT workloads.
Which of the following describes how using a data lakehouse
can help the data architect meet the needs of both workloads?
A data lakehouse stores unstructured data and is ACIDcompliant.
A data engineer has a Job with multiple tasks that runs nightly.
One of the tasks unexpectedly fails during 10 percent of the
runs.
Which of the following actions can the data engineer perform
to ensure the Job completes each night while minimizing
compute costs?
They can institute a retry policy for the task that periodically
fails
A data engineer has created a Delta table as part of a data
pipeline. Downstream data analysts now need SELECT
permission on the Delta table.
Assuming the data engineer is the Delta table owner, which part
of the Databricks Lakehouse Platform can the data engineer use
to grant the data analysts the appropriate access?
Data Explorer
A data engineer has ingested data from an external source into
a PySpark DataFrame raw_df. They need to briefly make this
data available in SQL for a data analyst to perform a quality
assurance check on the data.
Which of the following commands should the data engineer run
to make this data available in SQL for only the remainder of the
Spark session?
raw_df.createOrReplaceTempView("raw_df")
A data engineer has set up a notebook to automatically process
using a Job. The data engineer's manager wants to version
control the schedule due to its complexity.
Which of the following approaches can the data engineer use to
obtain a version-controllable configuration of the Job's
schedule?
They can download the JSON description of the Job from the
Job's page.
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