[Q82-Q99] Positive Aspects of ValidExamDumps Databricks-Certified-Data-Engineer-Associate Exam Dumps! [Dec-2025]

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Positive Aspects of Valid Dumps Databricks-Certified-Data-Engineer-Associate Exam Dumps! [Dec-2025]

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The Databricks Certified Data Engineer Associate Exam certification exam consists of 60 multiple-choice questions that need to be answered within 90 minutes. Databricks-Certified-Data-Engineer-Associate exam is available in English and is delivered online through the GAQM testing platform. Databricks Certified Data Engineer Associate Exam certification exam is open to all individuals who are interested in data engineering and Databricks, regardless of their educational background.

NO.82 Which of the following describes a scenario in which a data team will want to utilize cluster pools?

An automated report needs to be refreshed as quickly as possible.

An automated report needs to be made reproducible.

An automated report needs to be tested to identify errors.

An automated report needs to be version-controlled across multiple collaborators.

An automated report needs to be runnable by all stakeholders.

NO.83 A data architect has determined that a table of the following format is necessary:

Which of the following code blocks uses SQL DDL commands to create an empty Delta table in the above format regardless of whether a table already exists with this name?

Option A

Option B

Option C

Option D

Option E

NO.84 Which of the following must be specified when creating a new Delta Live Tables pipeline?

A key-value pair configuration

The preferred DBU/hour cost

A path to cloud storage location for the written data

A location of a target database for the written data

At least one notebook library to be executed

NO.85 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.86 A data engineer has been using a Databricks SQL dashboard to monitor the cleanliness of the input data to a data analytics dashboard for a retail use case. The job has a Databricks SQL query that returns the number of store-level records where sales is equal to zero. The data engineer wants their entire team to be notified via a messaging webhook whenever this value is greater than 0.
Which of the following approaches can the data engineer use to notify their entire team via a messaging webhook whenever the number of stores with $0 in sales is greater than zero?

They can set up an Alert with a custom template.

They can set up an Alert with a new email alert destination.

They can set up an Alert with one-time notifications.

They can set up an Alert with a new webhook alert destination.

They can set up an Alert without notifications.

NO.87 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.88 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.89 Which of the following commands can be used to write data into a Delta table while avoiding the writing of duplicate records?

DROP

IGNORE

MERGE

APPEND

INSERT

NO.90 A data engineer is designing an ETL pipeline to process both streaming and batch data from multiple sources The pipeline must ensure data quality, handle schema evolution, and provide easy maintenance. The team is considering using Delta Live Tables (DLT) in Databricks to achieve these goals. They want to understand the key features and benefits of DLT that make it suitable for this use case.
Why is Delta Live Tables (DLT) an appropriate choice?

Automatic data quality checks, built-in support for schema evolution, and declarative pipeline development

Manual schema enforcement, high operational overhead, and limited scalability

Requires custom code for data quality checks, no support for streaming data, and complex pipeline maintenance

Supports only batch processing, no data versioning, and high infrastructure costs

NO.91 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?

All datasets will be updated once and the pipeline will shut down. The compute resources will be terminated.

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.

All datasets will be updated once and the pipeline will persist without any processing. The compute resources will persist but go unused.

All datasets will be updated once and the pipeline will shut down. The compute resources will persist to allow for additional testing.

All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will persist to allow for additional testing.

NO.92 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.93 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?

Replace predict with a stream-friendly prediction function

Replace schema(schema) with option (“maxFilesPerTrigger”, 1)

Replace “transactions” with the path to the location of the Delta table

Replace format(“delta”) with format(“stream”)

Replace spark.read with spark.readStream

NO.94 Which of the following tools is used by Auto Loader process data incrementally?

Checkpointing

Spark Structured Streaming

Data Explorer

Unity Catalog

Databricks SQL

NO.95 A data engineer needs to combine sales data from an on-premises PostgreSQL database with customer data in Azure Synapse for a comprehensive report. The goal is to avoid data duplication and ensure up-to-date information How should the data engineer achieve this using Databricks?

Develop custom ETL pipelines to ingest data into Databricks

Use Lakehouse Federation to query both data sources directly

Manually synchronize data from both sources into a single database

Export data from both sources to CSV files and upload them to Databricks

NO.96 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.97 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

NO.98 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 Production 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?

All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will persist to allow for additional testing.

All datasets will be updated once and the pipeline will persist without any processing. The compute resources will persist but go unused.

All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will be deployed for the update and terminated when the pipeline is stopped.

All datasets will be updated once and the pipeline will shut down. The compute resources will be terminated.

All datasets will be updated once and the pipeline will shut down. The compute resources will persist to allow for additional testing.

NO.99 A platform team is creating a standardized template for Databricks Asset Bundles to support CI/CD. The template must specify defaults for artifacts, workspace root paths, and a run identity, while allowing a “dev” target to be the default and override specific paths.
How should the team use databricks.yml to satisfy these requirements?

Use deployment, builds, context, identity, and environments; set dev as default environment and override paths under builds.

Use roots, modules, profiles, actor, and targets; where profiles contain workspace and artifacts defaults and actor sets run identity.

Use project, packages, environment, identity, and stages; set dev as default stage and override workspace under environment.

Use bundle, artifacts, workspace, run_as, and targets at the top level; set one target with default: true and override workspace paths or artifacts under that target.

The GAQM Databricks-Certified-Data-Engineer-Associate (Databricks Certified Data Engineer Associate) Certification Exam is a highly sought-after certification in the field of data engineering. Databricks Certified Data Engineer Associate Exam certification is designed to validate the skills and knowledge required to design, build, and maintain data pipelines and data solutions on the Databricks platform. Databricks-Certified-Data-Engineer-Associate exam is intended for professionals who have a strong understanding of data engineering concepts and have experience working with Databricks.

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[Q82-Q99] Positive Aspects of ValidExamDumps Databricks-Certified-Data-Engineer-Associate Exam Dumps! [Dec-2025]

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