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Free Databricks-Certified-Professional-Data-Engineer Exam Braindumps
Certified Data Engineer Professional Questions 113–120 (page: 16)

Updated On: 16-Apr-2026
Page 16 of 44
PDF with 339 Questions

A Delta Lake table in the Lakehouse named customer_churn_params is used in churn prediction by the machine learning team. The table contains information about customers derived from a number of upstream sources. Currently, the data engineering team populates this table nightly by overwriting the table with the current valid values derived from upstream data sources.

Immediately after each update succeeds, the data engineering team would like to determine the difference between the new version and the previous version of the table.

Given the current implementation, which method can be used?

  1. Execute a query to calculate the difference between the new version and the previous version using Delta Lake's built-in versioning and lime travel functionality.
  2. Parse the Delta Lake transaction log to identify all newly written data files.
  3. Parse the Spark event logs to identify those rows that were updated, inserted, or deleted.
  4. Execute DESCRIBE HISTORY customer_churn_params to obtain the full operation metrics for the update, including a log of all records that have been added or modified.
  5. Use Delta Lake's change data feed to identify those records that have been updated, inserted, or deleted.

Answer(s): A



A data team's Structured Streaming job is configured to calculate running aggregates for item sales to update a downstream marketing dashboard. The marketing team has introduced a new promotion, and they would like to add a new field to track the number of times this promotion code is used for each item. A junior data engineer suggests updating the existing query as follows. Note that proposed changes are in bold.

Original query:



Proposed query:



Which step must also be completed to put the proposed query into production?

  1. Specify a new checkpointLocation
  2. Remove .option('mergeSchema', 'true') from the streaming write
  3. Increase the shuffle partitions to account for additional aggregates
  4. Run REFRESH TABLE delta./item_agg

Answer(s): A


Reference:



When using CLI or REST API to get results from jobs with multiple tasks, which statement correctly describes the response structure?

  1. Each run of a job will have a unique job_id; all tasks within this job will have a unique job_id
  2. Each run of a job will have a unique job_id; all tasks within this job will have a unique task_id
  3. Each run of a job will have a unique orchestration_id; all tasks within this job will have a unique run_id
  4. Each run of a job will have a unique run_id; all tasks within this job will have a unique task_id
  5. Each run of a job will have a unique run_id; all tasks within this job will also have a unique run_id

Answer(s): E



The data engineering team is configuring environments for development, testing, and production before beginning migration on a new data pipeline. The team requires extensive testing on both the code and data resulting from code execution, and the team wants to develop and test against data as similar to production data as possible.

A junior data engineer suggests that production data can be mounted to the development and testing environments, allowing pre-production code to execute against production data. Because all users have admin privileges in the development environment, the junior data engineer has offered to configure permissions and mount this data for the team.

Which statement captures best practices for this situation?

  1. All development, testing, and production code and data should exist in a single, unified workspace; creating separate environments for testing and development complicates administrative overhead.
  2. In environments where interactive code will be executed, production data should only be accessible with read permissions; creating isolated databases for each environment further reduces risks.
  3. As long as code in the development environment declares USE dev_db at the top of each notebook, there is no possibility of inadvertently committing changes back to production data sources.
  4. Because Delta Lake versions all data and supports time travel, it is not possible for user error or malicious actors to permanently delete production data; as such, it is generally safe to mount production data anywhere.
  5. Because access to production data will always be verified using passthrough credentials, it is safe to mount data to any Databricks development environment.

Answer(s): B



A data engineer, User A, has promoted a pipeline to production by using the REST API to programmatically create several jobs. A DevOps engineer, User B, has configured an external orchestration tool to trigger job runs through the REST API. Both users authorized the REST API calls using their personal access tokens.

A workspace admin, User C, inherits responsibility for managing this pipeline. User C uses the Databricks Jobs UI to take "Owner" privileges of each job. Jobs continue to be triggered using the credentials and tooling configured by User B.

An application has been configured to collect and parse run information returned by the REST API. Which statement describes the value returned in the creator_user_name field?

  1. Once User C takes "Owner" privileges, their email address will appear in this field; prior to this, User A's email address will appear in this field.
  2. User B's email address will always appear in this field, as their credentials are always used to trigger the run.
  3. User A's email address will always appear in this field, as they still own the underlying notebooks.
  4. Once User C takes "Owner" privileges, their email address will appear in this field; prior to this, User B's email address will appear in this field.
  5. User C will only ever appear in this field if they manually trigger the job, otherwise it will indicate User B.

Answer(s): B



A member of the data engineering team has submitted a short notebook that they wish to schedule as part of a larger data pipeline. Assume that the commands provided below produce the logically correct results when run as presented.



Which command should be removed from the notebook before scheduling it as a job?

  1. Cmd 2
  2. Cmd 3
  3. Cmd 4
  4. Cmd 5

Answer(s): D



Which statement regarding Spark configuration on the Databricks platform is true?

  1. The Databricks REST API can be used to modify the Spark configuration properties for an interactive cluster without interrupting jobs currently running on the cluster.
  2. Spark configurations set within a notebook will affect all SparkSessions attached to the same interactive cluster.
  3. When the same Spark configuration property is set for an interactive cluster and a notebook attached to that cluster, the notebook setting will always be ignored.
  4. Spark configuration properties set for an interactive cluster with the Clusters UI will impact all notebooks attached to that cluster.

Answer(s): D


Reference:



The business reporting team requires that data for their dashboards be updated every hour. The total processing time for the pipeline that extracts, transforms, and loads the data for their pipeline runs in 10 minutes.

Assuming normal operating conditions, which configuration will meet their service-level agreement requirements with the lowest cost?

  1. Configure a job that executes every time new data lands in a given directory
  2. Schedule a job to execute the pipeline once an hour on a new job cluster
  3. Schedule a Structured Streaming job with a trigger interval of 60 minutes
  4. Schedule a job to execute the pipeline once an hour on a dedicated interactive cluster

Answer(s): B



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