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Exam Sample Databricks Databricks-Certified-Data-Engineer-Professional Online - Databricks-Certified-Data-Engineer-Professional Valid Dumps Demo

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q29-Q34):

NEW QUESTION # 29
A Data engineer wants to run unit's tests using common Python testing frameworks on python functions defined across several Databricks notebooks currently used in production. How can the data engineer run unit tests against function that work with data in production?

  • A. Define and unit test functions using Files in Repos
  • B. Define and import unit test functions from a separate Databricks notebook
  • C. Run unit tests against non-production data that closely mirrors production
  • D. Define units test and functions within the same notebook

Answer: C

Explanation:
The best practice for running unit tests on functions that interact with data is to use a dataset that closely mirrors the production data. This approach allows data engineers to validate the logic of their functions without the risk of affecting the actual production data. It's important to have a representative sample of production data to catch edge cases and ensure the functions will work correctly when used in a production environment.


NEW QUESTION # 30
The DevOps team has configured a production workload as a collection of notebooks scheduled to run daily using the Jobs Ul. A new data engineering hire is onboarding to the team and has requested access to one of these notebooks to review the production logic. What are the maximum notebook permissions that can be granted to the user without allowing accidental changes to production code or data?

  • A. Can manage
  • B. Can Read
  • C. Can edit
  • D. Can run

Answer: B

Explanation:
Granting a user 'Can Read' permissions on a notebook within Databricks allows them to view the notebook's content without the ability to execute or edit it. This level of permission ensures that the new team member can review the production logic for learning or auditing purposes without the risk of altering the notebook's code or affecting production data and workflows. This approach aligns with best practices for maintaining security and integrity in production environments, where strict access controls are essential to prevent unintended modifications.


NEW QUESTION # 31
The data architect has mandated that all tables in the Lakehouse should be configured as external Delta Lake tables.
Which approach will ensure that this requirement is met?

  • A. Whenever a table is being created, make sure that the location keyword is used.
  • B. When tables are created, make sure that the external keyword is used in the create table statement.
  • C. When the workspace is being configured, make sure that external cloud object storage has been mounted.
  • D. When configuring an external data warehouse for all table storage. leverage Databricks for all ELT.
  • E. Whenever a database is being created, make sure that the location keyword is used Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from

Answer: A

Explanation:
This is the correct answer because it ensures that this requirement is met. The requirement is that all tables in the Lakehouse should be configured as external Delta Lake tables. An external table is a table that is stored outside of the default warehouse directory and whose metadata is not managed by Databricks. An external table can be created by using the location keyword to specify the path to an existing directory in a cloud storage system, such as DBFS or S3. By creating external tables, the data engineering team can avoid losing data if they drop or overwrite the table, as well as leverage existing data without moving or copying it.


NEW QUESTION # 32
A DLT pipeline includes the following streaming tables:
Raw_lot ingest raw device measurement data from a heart rate tracking device. Bgm_stats incrementally computes user statistics based on BPM measurements from raw_lot. How can the data engineer configure this pipeline to be able to retain manually deleted or updated records in the raw_iot table while recomputing the downstream table when a pipeline update is run?

  • A. Set the SkipChangeCommits flag to true raw_lot
  • B. Set the pipelines, reset, allowed property to false on raw_iot
  • C. Set the skipChangeCommits flag to true on bpm_stats
  • D. Set the pipelines, reset, allowed property to false on bpm_stats

Answer: B

Explanation:
In Databricks Lakehouse, to retain manually deleted or updated records in the raw_iot table while recomputing downstream tables when a pipeline update is run, the property pipelines.reset.allowed should be set to false. This property prevents the system from resetting the state of the table, which includes the removal of the history of changes, during a pipeline update. By keeping this property as false, any changes to the raw_iot table, including manual deletes or updates, are retained, and recomputation of downstream tables, such as bpm_stats, can occur with the full history of data changes intact.


NEW QUESTION # 33
A junior data engineer has configured a workload that posts the following JSON to the Databricks REST API endpoint 2.0/jobs/create.

Assuming that all configurations and referenced resources are available, which statement describes the result of executing this workload three times?

  • A. One new job named "Ingest new data" will be defined in the workspace, but it will not be executed.
  • B. Three new jobs named "Ingest new data" will be defined in the workspace, but no jobs will be executed.
  • C. The logic defined in the referenced notebook will be executed three times on the referenced existing all purpose cluster.
  • D. Three new jobs named "Ingest new data" will be defined in the workspace, and they will each run once daily.
  • E. The logic defined in the referenced notebook will be executed three times on new clusters with the configurations of the provided cluster ID.

Answer: B

Explanation:
Databricks jobs create will create a new job with the same name each time it is run.
In order to overwrite the extsting job you need to run databricks jobs reset


NEW QUESTION # 34
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