[2025] Databricks-Certified-Data-Engineer-Associate.pdf - Questions Answers PDF Sample Questions Reliable [Q15-Q36]

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[2025] Databricks-Certified-Data-Engineer-Associate.pdf – Questions Answers PDF Sample Questions Reliable

Databricks Databricks-Certified-Data-Engineer-Associate Dumps PDF Are going to be The Best Score

The GAQM Databricks-Certified-Data-Engineer-Associate (Databricks Certified Data Engineer Associate) Certification Exam is designed to validate the skills and knowledge of data engineers who work with the Databricks Unified Analytics Platform. Databricks Certified Data Engineer Associate Exam certification is ideal for professionals who want to demonstrate their expertise in building and optimizing data pipelines, data transformation, and data storage using Databricks.

The Databricks Databricks-Certified-Data-Engineer-Associate exam is intended for data engineers, data architects, and developers who are responsible for designing, building, and maintaining data pipelines. Databricks Certified Data Engineer Associate Exam certification exam is comprised of 60 multiple-choice questions, and candidates have 90 minutes to complete the exam. Databricks-Certified-Data-Engineer-Associate exam measures candidates’ knowledge and skills in various areas, including data ingestion, data transformation, and data processing.

NO.15 A data engineer needs to apply custom logic to string column city in table stores for a specific use case. In order to apply this custom logic at scale, the data engineer wants to create a SQL user-defined function (UDF).
Which of the following code blocks creates this SQL UDF?

NO.16 A data engineer has three tables in a Delta Live Tables (DLT) pipeline. They have configured the pipeline to drop invalid records at each table. They notice that some data is being dropped due to quality concerns at some point in the DLT pipeline. They would like to determine at which table in their pipeline the data is being dropped.
Which of the following approaches can the data engineer take to identify the table that is dropping the records?

They can set up separate expectations for each table when developing their DLT pipeline.

They cannot determine which table is dropping the records.

They can set up DLT to notify them via email when records are dropped.

They can navigate to the DLT pipeline page, click on each table, and view the data quality statistics.

They can navigate to the DLT pipeline page, click on the “Error” button, and review the present errors.

NO.17 Which of the following is a benefit of the Databricks Lakehouse Platform embracing open source technologies?

Cloud-specific integrations

Simplified governance

Ability to scale storage

Ability to scale workloads

Avoiding vendor lock-in

NO.18 Which of the following is stored in the Databricks customer’s cloud account?

Databricks web application

Cluster management metadata

Repos

Data

Notebooks

NO.19 Which of the following tools is used by Auto Loader process data incrementally?

Checkpointing

Spark Structured Streaming

Data Explorer

Unity Catalog

Databricks SQL

NO.20 A data engineer is designing a data pipeline. The source system generates files in a shared directory that is also used by other processes. As a result, the files should be kept as is and will accumulate in the directory. The data engineer needs to identify which files are new since the previous run in the pipeline, and set up the pipeline to only ingest those new files with each run.
Which of the following tools can the data engineer use to solve this problem?

Unity Catalog

Delta Lake

Databricks SQL

Data Explorer

Auto Loader

NO.21 A data engineer has joined an existing project and they see the following query in the project repository:
CREATE STREAMING LIVE TABLE loyal_customers AS
SELECT customer_id –
FROM STREAM(LIVE.customers)
WHERE loyalty_level = ‘high’;
Which of the following describes why the STREAM function is included in the query?

The STREAM function is not needed and will cause an error.

The table being created is a live table.

The customers table is a streaming live table.

The customers table is a reference to a Structured Streaming query on a PySpark DataFrame.

The data in the customers table has been updated since its last run.

NO.22 A data engineer needs to determine whether to use the built-in Databricks Notebooks versioning or version their project using Databricks Repos.
Which of the following is an advantage of using Databricks Repos over the Databricks Notebooks versioning?

Databricks Repos automatically saves development progress

Databricks Repos supports the use of multiple branches

Databricks Repos allows users to revert to previous versions of a notebook

Databricks Repos provides the ability to comment on specific changes

Databricks Repos is wholly housed within the Databricks Lakehouse Platform

NO.23 In order for Structured Streaming to reliably track the exact progress of the processing so that it can handle any kind of failure by restarting and/or reprocessing, which of the following two approaches is used by Spark to record the offset range of the data being processed in each trigger?

Checkpointing and Write-ahead Logs

Structured Streaming cannot record the offset range of the data being processed in each trigger.

Replayable Sources and Idempotent Sinks

Write-ahead Logs and Idempotent Sinks

Checkpointing and Idempotent Sinks

NO.24 A data engineer has a Python variable table_name that they would like to use in a SQL query. They want to construct a Python code block that will run the query using table_name.
They have the following incomplete code block:
____(f”SELECT customer_id, spend FROM {table_name}”)
Which of the following can be used to fill in the blank to successfully complete the task?

spark.delta.sql

spark.delta.table

spark.table

dbutils.sql

spark.sql

NO.25 Which of the following benefits is provided by the array functions from Spark SQL?

An ability to work with data in a variety of types at once

An ability to work with data within certain partitions and windows

An ability to work with time-related data in specified intervals

An ability to work with complex, nested data ingested from JSON files

An ability to work with an array of tables for procedural automation

NO.26 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?

All of the fields had at least one null value

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

JSON data is a text-based format

Auto Loader only works with string data

Auto Loader cannot infer the schema of ingested data

NO.27 A data engineer only wants to execute the final block of a Python program if the Python variable day_of_week is equal to 1 and the Python variable review_period is True.
Which of the following control flow statements should the data engineer use to begin this conditionally executed code block?

if day_of_week = 1 and review_period:

if day_of_week = 1 and review_period = “True”:

if day_of_week == 1 and review_period == “True”:

if day_of_week == 1 and review_period:

if day_of_week = 1 & review_period: = “True”:

NO.28 A data engineer is working with two tables. Each of these tables is displayed below in its entirety.

The data engineer runs the following query to join these tables together:

Which of the following will be returned by the above query?

Option A

Option B

Option C

Option D

Option E

NO.29 A data engineer is designing a data pipeline. The source system generates files in a shared directory that is also used by other processes. As a result, the files should be kept as is and will accumulate in the directory. The data engineer needs to identify which files are new since the previous run in the pipeline, and set up the pipeline to only ingest those new files with each run.
Which of the following tools can the data engineer use to solve this problem?

Unity Catalog

Data Explorer

Databricks SQL

Delta Lake

Auto Loader

NO.30 A data engineer has left the organization. The data team needs to transfer ownership of the data engineer’s Delta tables to a new data engineer. The new data engineer is the lead engineer on the data team.
Assuming the original data engineer no longer has access, which of the following individuals must be the one to transfer ownership of the Delta tables in Data Explorer?

Databricks account representative

This transfer is not possible

Workspace administrator

New lead data engineer

Original data engineer

NO.31 Which of the following commands will return the location of database customer360?

DESCRIBE LOCATION customer360;

DROP DATABASE customer360;

DESCRIBE DATABASE customer360;

ALTER DATABASE customer360 SET DBPROPERTIES (‘location’ = ‘/user’};

USE DATABASE customer360;

NO.32 In which of the following scenarios should a data engineer use the MERGE INTO command instead of the INSERT INTO command?

When the location of the data needs to be changed

When the target table is an external table

When the source table can be deleted

When the target table cannot contain duplicate records

When the source is not a Delta table

NO.33 Which of the following describes when to use the CREATE STREAMING LIVE TABLE (formerly CREATE INCREMENTAL LIVE TABLE) syntax over the CREATE LIVE TABLE syntax when creating Delta Live Tables (DLT) tables using SQL?

CREATE STREAMING LIVE TABLE should be used when the subsequent step in the DLT pipeline is static.

CREATE STREAMING LIVE TABLE should be used when data needs to be processed incrementally.

CREATE STREAMING LIVE TABLE is redundant for DLT and it does not need to be used.

CREATE STREAMING LIVE TABLE should be used when data needs to be processed through complicated aggregations.

CREATE STREAMING LIVE TABLE should be used when the previous step in the DLT pipeline is static.

NO.34 A data engineer has configured a Structured Streaming job to read from a table, manipulate the data, and then perform a streaming write into a new table.
The code block used by the data engineer is below:

If the data engineer only wants the query to process all of the available data in as many batches as required, which of the following lines of code should the data engineer use to fill in the blank?

processingTime(1)

trigger(availableNow=True)

trigger(parallelBatch=True)

trigger(processingTime=”once”)

trigger(continuous=”once”)

NO.35 A data engineer has joined an existing project and they see the following query in the project repository:
CREATE STREAMING LIVE TABLE loyal_customers AS
SELECT customer_id –
FROM STREAM(LIVE.customers)
WHERE loyalty_level = ‘high’;
Which of the following describes why the STREAM function is included in the query?

The STREAM function is not needed and will cause an error.

The table being created is a live table.

The customers table is a streaming live table.

The customers table is a reference to a Structured Streaming query on a PySpark DataFrame.

The data in the customers table has been updated since its last run.

NO.36 A data engineer has realized that they made a mistake when making a daily update to a table. They need to use Delta time travel to restore the table to a version that is 3 days old. However, when the data engineer attempts to time travel to the older version, they are unable to restore the data because the data files have been deleted.
Which of the following explains why the data files are no longer present?

The VACUUM command was run on the table

The TIME TRAVEL command was run on the table

The DELETE HISTORY command was run on the table

The OPTIMIZE command was nun on the table

The HISTORY command was run on the table