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Free Databricks Databricks-Certified-Data-Engineer-Associate Exam Actual Questions

The questions for Databricks-Certified-Data-Engineer-Associate were last updated On Jun 10, 2025

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Question No. 1

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?

ACREATE TABLE all_transactions AS
SELECT * FROM march_transactions
INNER JOIN SELECT * FROM april_transactions;

BCREATE TABLE all_transactions AS
SELECT * FROM march_transactions
UNION SELECT * FROM april_transactions;

CCREATE TABLE all_transactions AS
SELECT * FROM march_transactions
OUTER JOIN SELECT * FROM april_transactions;

DCREATE TABLE all_transactions AS
SELECT * FROM march_transactions
INTERSECT SELECT * from april_transactions;

ECREATE TABLE all_transactions AS
SELECT * FROM march_transactions
MERGE SELECT * FROM april_transactions;

Show Answer

Correct Answer: B

The correct command to create a new table that contains all records from two tables without duplicate records is to use theUNIONoperator. The UNION operator combines the results of two queries and removes any duplicate rows. The INNER JOIN, OUTER JOIN, and MERGE operators do not remove duplicate rows, and the INTERSECT operator only returns the rows that are common to both tables. Therefore, option B is the only correct answer.Reference:Databricks SQL Reference - UNION,Databricks SQL Reference - JOIN,Databricks SQL Reference - MERGE, [Databricks SQL Reference - INTERSECT]

Question No. 2

A data engineer is using the following code block as part of a batch ingestion pipeline to read from a composable table:

Which of the following changes needs to be made so this code block will work when the transactions table is a stream source?

AReplace predict with a stream-friendly prediction function

BReplace schema(schema) with option ('maxFilesPerTrigger', 1)

CReplace 'transactions' with the path to the location of the Delta table

DReplace format('delta') with format('stream')

EReplace spark.read with spark.readStream

Show Answer

Correct Answer: E

: To read from a stream source, the data engineer needs to use the spark.readStream method instead of the spark.read method. The spark.readStream method returns a DataStreamReader object that can be used to specify the details of the input source, such as the format, the schema, the path, and the options. The spark.read method is only suitable for batch processing, not streaming processing. The other changes are not necessary or correct for reading from a stream source.Reference:Structured Streaming Programming Guide,Read a stream,Databricks Data Sources

Question No. 3

A data engineer has developed a data pipeline to ingest data from a JSON source using Auto Loader, but the engineer has not provided any type inference or schema hints in their pipeline. Upon reviewing the data, the data engineer has noticed that all of the columns in the target table are of the string type despite some of the fields only including float or boolean values.

Which of the following describes why Auto Loader inferred all of the columns to be of the string type?

AThere was a type mismatch between the specific schema and the inferred schema

BJSON data is a text-based format

CAuto Loader only works with string data

DAll of the fields had at least one null value

EAuto Loader cannot infer the schema of ingested data

Show Answer

Correct Answer: B

JSON data is a text-based format that represents data as a collection of name-value pairs. By default, when Auto Loader infers the schema of JSON data, it treats all columns as strings. This is because JSON data can have varying data types for the same column across different files or records, and Auto Loader does not attempt to reconcile these differences. For example, a column named ''age'' may have integer values in some files, but string values in others. To avoid data loss or errors, Auto Loader infers the column as a string type. However, Auto Loader also provides an option to infer more precise column types based on the sample data. This option is called cloudFiles.inferColumnTypes and it can be set to true or false. When set to true, Auto Loader tries to infer the exact data types of the columns, such as integers, floats, booleans, or nested structures. When set to false, Auto Loader infers all columns as strings. The default value of this option is false.Reference:Configure schema inference and evolution in Auto Loader,Schema inference with auto loader (non-DLT and DLT),Using and Abusing Auto Loader's Inferred Schema,Explicit path to data or a defined schema required for Auto loader.

Question No. 4

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?

AThey can schedule the query to refresh every 1 day from the SQL endpoint's page in Databricks SQL.

BThey can schedule the query to refresh every 12 hours from the SQL endpoint's page in Databricks SQL.

CThey can schedule the query to refresh every 1 day from the query's page in Databricks SQL.

DThey can schedule the query to run every 1 day from the Jobs UI.

EThey can schedule the query to run every 12 hours from the Jobs UI.

Show Answer

Correct Answer: C

Databricks SQL allows users to schedule queries to run automatically at a specified frequency and time zone. This can help users to keep their dashboards or alerts updated with the latest data. To schedule a query, users need to do the following steps:

In the Query Editor, click Schedule > Add schedule to open a menu with schedule settings.

Choose when to run the query. Use the dropdown pickers to specify the frequency, period, starting time, and time zone. Optionally, select the Show cron syntax checkbox to edit the schedule in Quartz Cron Syntax.

Choose More options to show optional settings. Users can also choose a name for the schedule, and a SQL warehouse to power the query.

Click Create. The query will run automatically according to the schedule.

The other options are incorrect because they do not refer to the correct location or frequency to schedule the query. The query's page in Databricks SQL is the place where users can edit, run, or schedule the query. The SQL endpoint's page in Databricks SQL is the place where users can manage the SQL warehouses and SQL endpoints. The Jobs UI is the place where users can create, run, or schedule jobs that execute notebooks, JARs, or Python scripts.Reference:Schedule a query,What are Databricks SQL alerts?,Jobs.

Question No. 5

A data engineer wants to schedule their Databricks SQL dashboard to refresh every hour, but they only want the associated SQL endpoint to be running when It is necessary. The dashboard has multiple queries on multiple datasets associated with it. The data that feeds the dashboard is automatically processed using a Databricks Job.

Which approach can the data engineer use to minimize the total running time of the SQL endpoint used in the refresh schedule of their dashboard?

AO They can reduce the cluster size of the SQL endpoint.

BQ They can turn on the Auto Stop feature for the SQL endpoint.

CO They can set up the dashboard's SQL endpoint to be serverless.

D0 They can ensure the dashboard's SQL endpoint matches each of the queries' SQL endpoints.

Show Answer

Correct Answer: B

To minimize the total running time of the SQL endpoint used in the refresh schedule of a dashboard in Databricks, the most effective approach is to utilize the Auto Stop feature. This feature allows the SQL endpoint to automatically stop after a period of inactivity, ensuring that it only runs when necessary, such as during the dashboard refresh or when actively queried. This minimizes resource usage and associated costs by ensuring the SQL endpoint is not running idle outside of these operations.

Reference: Databricks documentation on SQL endpoints: SQL Endpoints in Databricks