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The Databricks Certified Data Engineer Associate Exam certification exam tests the candidate's knowledge of Databricks, Apache Spark, and data engineering concepts. It covers various topics such as data ingestion, data processing, data transformation, and data storage. Databricks-Certified-Data-Engineer-Associate Exam also evaluates the candidate's ability to design and implement data pipelines using Databricks, as well as their knowledge of best practices for data management and governance.

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The GAQM Databricks-Certified-Data-Engineer-Associate (Databricks Certified Data Engineer Associate) certification exam is designed for professionals who want to demonstrate their expertise in building and maintaining data pipelines on the Databricks platform. Databricks-Certified-Data-Engineer-Associate exam validates the skills and knowledge required to design, build, and maintain data pipelines on Databricks, and is a recognition of the candidate's ability to work with big data technologies and tools.

Databricks Certified Data Engineer Associate certification is offered by GAQM, a leading provider of professional certifications for IT and business professionals. GAQM has a reputation for providing high-quality certification exams that are recognized by employers and professionals around the world. The Databricks Certified Data Engineer Associate certification is no exception.

Databricks Certified Data Engineer Associate Exam Sample Questions (Q180-Q185):

NEW QUESTION # 180
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?

A. Write-ahead Logs and Idempotent Sinks
B. Checkpointing and Write-ahead Logs
C. Checkpointing and Idempotent Sinks
D. Replayable Sources and Idempotent Sinks
E. Structured Streaming cannot record the offset range of the data being processed in each trigger.

Answer: B

Explanation:
Structured Streaming uses checkpointing and write-ahead logs to record the offset range of the data being processed in each trigger. This ensures that the engine can reliably track the exact progress of the processing and handle any kind of failure by restarting and/or reprocessing. Checkpointing is the mechanism of saving the state of a streaming query to fault-tolerant storage (such as HDFS) so that it can be recovered after a failure.
Write-ahead logs are files that record the offset range of the data being processed in each trigger and are written to the checkpoint location before the processing starts. These logs are used to recover the query state and resume processing from the last processed offset range in case of a failure. References: Structured Streaming Programming Guide, Fault Tolerance Semantics

NEW QUESTION # 181
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?

A. The DELETE HISTORY command was run on the table
B. The TIME TRAVEL command was run on the table
C. The OPTIMIZE command was nun on the table
D. The VACUUM command was run on the table
E. The HISTORY command was run on the table

Answer: D

Explanation:
Explanation
The VACUUM command in Delta Lake is used to clean up and remove unnecessary data files that are no longer needed for time travel or query purposes. When you run VACUUMwith certain retention settings, it can delete older data files, which might include versions of data that are older than the specified retention period. If the data engineer is unable to restore the table to a version that is 3 days old because the data files have been deleted, it's likely because the VACUUM command was run on the table, removing the older data files as part of data cleanup.

NEW QUESTION # 182
A data engineer is maintaining an ETL pipeline code with a GitHub repository linked to their Databricks account. The data engineer wants to deploy the ETL pipeline to production as a databricks workflow.
Which approach should the data engineer use?

A. Manually create and manage the workflow in Ul
B. Maintain workflow_conf ig. json and deploy it using Terraform
C. Databricks Asset Bundles (DAB) + GitHub Integration
D. Maintain workflow_config.j son and deploy it using Databricks CLI

Answer: C

NEW QUESTION # 183
A data engineer is working on a Databricks project that utilizes cloud storage. The data engineer wants to load several json files from containers on a storage account as soon as the file arrives within the storage account.
Which syntax should the data engineer follow to first load the files into a dataframe and check that it is working as expected using Python?

A. df = spark.readStream.format( " cloudFiles " ) .option( " cloudFiles.format " , " json " ) .load( " /input
/path " )
B. df = spark.readStream.format( " json " ).load( " input/path " )
C. df = spark.readStream.format( " cloud " ),option( " json " ).load( " /input/path " )
D. df = spark.read.json( " inp i./path " )

Answer: A

NEW QUESTION # 184
A data engineer has finished developing a new Declarative Automation Bundle, formerly known as a Databricks Asset Bundle, for a streaming pipeline. The engineer must verify that the configuration is syntactically correct, deploy the code to the workspace, and finally trigger the pipeline to confirm that it runs as expected.
Which sequence of Databricks CLI commands performs these steps in the correct order?

A. databricks bundle validate → databricks bundle deploy → databricks bundle run
B. databricks bundle init → databricks bundle deploy → databricks bundle run
C. databricks bundle check → databricks bundle upload → databricks bundle start
D. databricks bundle validate → databricks bundle sync → databricks bundle execute

Answer: A

NEW QUESTION # 185
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