PASS GUARANTEED QUIZ 2025 DATABRICKS DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-ENGINEER–THE BEST TEST ANSWERS

Pass Guaranteed Quiz 2025 Databricks Databricks-Certified-Professional-Data-Engineer–The Best Test Answers

Pass Guaranteed Quiz 2025 Databricks Databricks-Certified-Professional-Data-Engineer–The Best Test Answers

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q84-Q89):

NEW QUESTION # 84
The data architect has mandated that all tables in the Lakehouse should be configured as external (also known as "unmanaged") Delta Lake tables.
Which approach will ensure that this requirement is met?

  • A. When data is saved to a table, make sure that a full file path is specified alongside the Delta format.
  • B. When tables are created, make sure that the EXTERNAL keyword is used in the CREATE TABLE statement.
  • C. When a database is being created, make sure that the LOCATION keyword is used.
  • D. When configuring an external data warehouse for all table storage, leverage Databricks for all ELT.
  • E. When the workspace is being configured, make sure that external cloud object storage has been mounted.

Answer: B

Explanation:
To create an external or unmanaged Delta Lake table, you need to use the EXTERNAL keyword in the CREATE TABLE statement. This indicates that the table is not managed by the catalog and the data files are not deleted when the table is dropped. You also need to provide a LOCATION clause to specify the path where the data files are stored. For example:
CREATE EXTERNAL TABLE events ( date DATE, eventId STRING, eventType STRING, data STRING) USING DELTA LOCATION '/mnt/delta/events'; This creates an external Delta Lake table named events that references the data files in the '/mnt/delta/events' path. If you drop this table, the data files will remain intact and you can recreate the table with the same statement.
References:
* https://docs.databricks.com/delta/delta-batch.html#create-a-table
* https://docs.databricks.com/delta/delta-batch.html#drop-a-table


NEW QUESTION # 85
Which Python variable contains a list of directories to be searched when trying to locate required modules?

  • A. pypi.path
  • B. ,sys.path
  • C. importlib.resource path
  • D. pylib.source
  • E. os-path

Answer: B


NEW QUESTION # 86
Which of the following describes how Databricks Repos can help facilitate CI/CD workflows on the
Databricks Lakehouse Platform?

  • A. Databricks Repos can merge changes from a secondary Git branch into a main Git branch
  • B. Databricks Repos can be used to design, develop, and trigger Git automation pipelines
  • C. Databricks Repos can commit or push code changes to trigger a CI/CD process
  • D. Databricks Repos can facilitate the pull request, review, and approval process before merging branches
  • E. Databricks Repos can store the single-source-of-truth Git repository

Answer: C


NEW QUESTION # 87
In order to facilitate near real-time workloads, a data engineer is creating a helper function to leverage the schema detection and evolution functionality of Databricks Auto Loader. The desired function will automatically detect the schema of the source directly, incrementally process JSON files as they arrive in a source directory, and automatically evolve the schema of the table when new fields are detected.
The function is displayed below with a blank:

Which response correctly fills in the blank to meet the specified requirements?

  • A. Option E
  • B. Option B
  • C. Option D
  • D. Option C
  • E. Option A

Answer: B

Explanation:
Option B correctly fills in the blank to meet the specified requirements. Option B uses the "cloudFiles.
schemaLocation" option, which is required for the schema detection andevolution functionality of Databricks Auto Loader. Additionally, option B uses the "mergeSchema" option, which is required for the schema evolution functionality of Databricks Auto Loader. Finally, option B uses the "writeStream" method, which is required for the incremental processing of JSON files as they arrive in a source directory. The other options are incorrect because they either omit the required options, use the wrong method, or use the wrong format. References:
* Configure schema inference and evolution in Auto Loader: https://docs.databricks.com/en/ingestion
/auto-loader/schema.html
* Write streaming data: https://docs.databricks.com/spark/latest/structured-streaming/writing-streaming- data.html


NEW QUESTION # 88
A data engineer needs to capture pipeline settings from an existing in the workspace, and use them to create and version a JSON file to create a new pipeline.
Which command should the data engineer enter in a web terminal configured with the Databricks CLI?

  • A. Use the alone command to create a copy of an existing pipeline; use the get JSON command to get the pipeline definition; save this to git
  • B. Use the get command to capture the settings for the existing pipeline; remove the pipeline_id and rename the pipeline; use this in a create command
  • C. Use list pipelines to get the specs for all pipelines; get the pipeline spec from the return results parse and use this to create a pipeline
  • D. Stop the existing pipeline; use the returned settings in a reset command

Answer: B

Explanation:
The Databricks CLI provides a way to automate interactions with Databricks services. When dealing with pipelines, you can use thedatabricks pipelines get --pipeline-idcommand to capture the settings of an existing pipeline in JSON format. This JSON can then be modified by removing thepipeline_idto prevent conflicts and renaming the pipeline to create a new pipeline. The modified JSON file can then be used with the databricks pipelines createcommand to create a new pipeline with those settings.
References:
* Databricks Documentation on CLI for Pipelines: Databricks CLI - Pipelines


NEW QUESTION # 89
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