Databricks Databricks Certified Data Engineer Associate Practice Exams
Last updated on Oct 22,2026- Exam Code: Databricks Certified Data Engineer Associate
- Exam Name: Databricks Certified Data Engineer Associate Exam
- Certification Provider: Databricks
- Latest update: Oct 22,2026
A data analysis team has noticed that their Databricks SQL queries are running too slowly when connected to their always-on SQL endpoint. They claim that this issue is present when many members of the team are running small queries simultaneously. They ask the data engineering team for help. The data engineering team notices that each of the team’s queries uses the same SQL endpoint.
Which of the following approaches can the data engineering team use to improve the latency of the team’s queries?
- A . They can increase the cluster size of the SQL endpoint.
- B . They can increase the maximum bound of the SQL endpoint’s scaling range.
- C . They can turn on the Auto Stop feature for the SQL endpoint.
- D . They can turn on the Serverless feature for the SQL endpoint.
- E . They can turn on the Serverless feature for the SQL endpoint and change the Spot Instance Policy to “Reliability Optimized.”
A data engineer has been using a Databricks SQL dashboard to monitor the cleanliness of the input data to an ELT job. The ELT job has its Databricks SQL query that returns the number of input records containing unexpected NULL values. The data engineer wants their entire team to be notified via a messaging webhook whenever this value reaches 100.
Which of the following approaches can the data engineer use to notify their entire team via a messaging webhook whenever the number of NULL values reaches 100?
- A . They can set up an Alert with a custom template.
- B . They can set up an Alert with a new email alert destination.
- C . They can set up an Alert with a new webhook alert destination.
- D . They can set up an Alert with one-time notifications.
- E . They can set up an Alert without notifications.
A dataset has been defined using Delta Live Tables and includes an expectations clause:
CONSTRAINT valid_timestamp EXPECT (timestamp > ‘2020-01-01’) ON VIOLATION FAIL UPDATE
What is the expected behavior when a batch of data containing data that violates these constraints is processed?
- A . Records that violate the expectation are dropped from the target dataset and recorded as invalid in the event log.
- B . Records that violate the expectation cause the job to fail.
- C . Records that violate the expectation are dropped from the target dataset and loaded into a quarantine table.
- D . Records that violate the expectation are added to the target dataset and recorded as invalid in the event log.
- E . Records that violate the expectation are added to the target dataset and flagged as invalid in a field added to the target dataset.
In Databricks, how do you access a Delta table’s previous version using time travel functionality?
- A . SELECT * FROM table TIMESTAMP AS OF ‘2021-01-01’
- B . SELECT * FROM table VERSION AS OF 5
- C . SELECT * FROM table WHERE version = 5
- D . SELECT * FROM table AT ‘2021-01-01’
- E . SELECT * FROM table AS OF ‘2021-01-01’
A data engineering team has two tables. The first table march_transactions is a collection of all retail transactions in the month of March. The second table april_transactions is a collection of all retail
transactions in the month of April. There are no duplicate records between the tables.
Which of the following commands should be run to create a new table all_transactions that contains all records from march_transactions and april_transactions without duplicate records?
- A . CREATE TABLE all_transactions AS
SELECT * FROM march_transactions
INNER JOIN SELECT * FROM april_transactions; - B . CREATE TABLE all_transactions AS
SELECT * FROM march_transactions
UNION SELECT * FROM april_transactions; - C . CREATE TABLE all_transactions AS
SELECT * FROM march_transactions
OUTER JOIN SELECT * FROM april_transactions; - D . CREATE TABLE all_transactions AS
SELECT * FROM march_transactions
INTERSECT SELECT * from april_transactions; - E . CREATE TABLE all_transactions AS
SELECT * FROM march_transactions
MERGE SELECT * FROM april_transactions;
Which feature provides ACID transactionality in Databricks?
- A . Spark SQL
- B . Delta Lake
- C . Databricks SQL
- D . MLlib
An engineering manager uses a Databricks SQL query to monitor ingestion latency for each data source. The manager checks the results of the query every day, but they are manually rerunning the query each day and waiting for the results.
Which of the following approaches can the manager use to ensure the results of the query are updated each day?
- A . They can schedule the query to refresh every 1 day from the SQL endpoint’s page in Databricks SQL.
- B . They can schedule the query to refresh every 12 hours from the SQL endpoint’s page in Databricks SQL.
- C . They can schedule the query to refresh every 1 day from the query’s page in Databricks SQL.
- D . They can schedule the query to run every 1 day from the Jobs UI.
- E . They can schedule the query to run every 12 hours from the Jobs UI.
A Delta Live Table pipeline includes two datasets defined using STREAMING LIVE TABLE. Three datasets are defined against Delta Lake table sources using LIVE TABLE.
The table is configured to run in Development mode using the Continuous Pipeline Mode.
Assuming previously unprocessed data exists and all definitions are valid, what is the expected outcome after clicking Start to update the pipeline?
- A . All datasets will be updated once and the pipeline will shut down. The compute resources will be terminated.
- B . All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will persist until the pipeline is shut down.
- C . All datasets will be updated once and the pipeline will persist without any processing. The compute resources will persist but go unused.
- D . All datasets will be updated once and the pipeline will shut down. The compute resources will persist to allow for additional testing.
- E . All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will persist to allow for additional testing.
What is required to initiate streaming data processing in Databricks using structured streaming?
- A . A trigger setting
- B . A Delta table
- C . A DataFrame read operation
- D . An action on the stream
A data analyst has a series of queries in a SQL program. The data analyst wants this program to run every day. They only want the final query in the program to run on Sundays. They ask for help from the data engineering team to complete this task.
Which of the following approaches could be used by the data engineering team to complete this task?
- A . They could submit a feature request with Databricks to add this functionality.
- B . They could wrap the queries using PySpark and use Python’s control flow system to determine when to run the final query.
- C . They could only run the entire program on Sundays.
- D . They could automatically restrict access to the source table in the final query so that it is only accessible on Sundays.
- E . They could redesign the data model to separate the data used in the final query into a new table.