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Databricks Certified Data Engineer Professional Exam Sample Questions (Q23-Q28):

NEW QUESTION # 23
A team of data engineer are adding tables to a DLT pipeline that contain repetitive expectations for many of the same data quality checks.
One member of the team suggests reusing these data quality rules across all tables defined for this pipeline.
What approach would allow them to do this?

A. Maintain data quality rules in a separate Databricks notebook that each DLT notebook of file.
B. Use global Python variables to make expectations visible across DLT notebooks included in the same pipeline.
C. Add data quality constraints to tables in this pipeline using an external job with access to pipeline configuration files.
D. Maintain data quality rules in a Delta table outside of this pipeline's target schema, providing the schema name as a pipeline parameter.

Answer: D

Explanation:
Maintaining data quality rules in a centralized Delta table allows for the reuse of these rules across multiple DLT (Delta Live Tables) pipelines. By storing these rules outside the pipeline's target schema and referencing the schema name as a pipeline parameter, the team can apply the same set of data quality checks to different tables within the pipeline. This approach ensures consistency in data quality validations and reduces redundancy in code by not having to replicate the same rules in each DLT notebook or file.

NEW QUESTION # 24
A Databricks job has been configured with 3 tasks, each of which is a Databricks notebook. Task A does not depend on other tasks. Tasks B and C run in parallel, with each having a serial dependency on task A.
If tasks A and B complete successfully but task C fails during a scheduled run, which statement describes the resulting state?

A. Because all tasks are managed as a dependency graph, no changes will be committed to the Lakehouse until ail tasks have successfully been completed.
B. All logic expressed in the notebook associated with tasks A and B will have been successfully completed; any changes made in task C will be rolled back due to task failure.
C. All logic expressed in the notebook associated with task A will have been successfully completed; tasks B and C will not commit any changes because of stage failure.
D. All logic expressed in the notebook associated with tasks A and B will have been successfully completed; some operations in task C may have completed successfully.
E. Unless all tasks complete successfully, no changes will be committed to the Lakehouse; because task C failed, all commits will be rolled back automatically.

Answer: D

Explanation:
The query uses the CREATE TABLE USING DELTA syntax to create a Delta Lake table from an existing Parquet file stored in DBFS. The query also uses the LOCATION keyword to specify the path to the Parquet file as /mnt/finance_eda_bucket/tx_sales.parquet. By using the LOCATION keyword, the query creates an external table, which 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 from an existing directory in a cloud storage system, such as DBFS or S3, that contains data files in a supported format, such as Parquet or CSV.
The resulting state after running the second command is that an external table will be created in the storage container mounted to /mnt/finance_eda_bucket with the new name prod.sales_by_store. The command will not change any data or move any files in the storage container; it will only update the table reference in the metastore and create a new Delta transaction log for the renamed table.

NEW QUESTION # 25
A data architect has heard about lake's built-in versioning and time travel capabilities. For auditing purposes they have a requirement to maintain a full of all valid street addresses as they appear in the customers table.
The architect is interested in implementing a Type 1 table, overwriting existing records with new values and relying on Delta Lake time travel to support long-term auditing. A data engineer on the project feels that a Type 2 table will provide better performance and scalability. Which piece of Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from information is critical to this decision?

A. Shallow clones can be combined with Type 1 tables to accelerate historic queries for long-term versioning.
B. Delta Lake time travel does not scale well in cost or latency to provide a long-term versioning solution.
C. Delta Lake time travel cannot be used to query previous versions of these tables because Type 1 changes modify data files in place.
D. Data corruption can occur if a query fails in a partially completed state because Type 2 tables requires setting multiple fields in a single update.
E. Delta Lake only supports Type 0 tables; once records are inserted to a Delta Lake table, they cannot be modified.

Answer: B

Explanation:
Delta Lake's time travel feature allows users to access previous versions of a table, providing a powerful tool for auditing and versioning. However, using time travel as a long-term versioning solution for auditing purposes can be less optimal in terms of cost and performance, especially as the volume of data and the number of versions grow. For maintaining a full history of valid street addresses as they appear in a customers table, using a Type 2 table (where each update creates a new record with versioning) might provide better scalability and performance by avoiding the overhead associated with accessing older versions of a large table. While Type 1 tables, where existing records are overwritten with new values, seem simpler and can leverage time travel for auditing, the critical piece of information is that time travel might not scale well in cost or latency for long-term versioning needs, making a Type 2 approach more viable for performance and scalability.

NEW QUESTION # 26
A developer has successfully configured credential for Databricks Repos and cloned a remote Git repository. Hey don not have privileges to make changes to the main branch, which is the only branch currently visible in their workspace.
Use Response to pull changes from the remote Git repository commit and push changes to a branch that appeared as a changes were pulled.

A. Use repos to create a fork of the remote repository commit all changes and make a pull request on the source repository
B. Use repos to merge all difference and make a pull request back to the remote repository.
C. Use Repos to create a new branch commit all changes and push changes to the remote Git repertory.
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from
D. Use Repos to merge all differences and make a pull request back to the remote repository.
E. Use Repos to pull changes from the remote Git repository; commit and push changes to a branch that appeared as changes were pulled.

Answer: C

Explanation:
In Databricks Repos, when a user does not have privileges to make changes directly to the main branch of a cloned remote Git repository, the recommended approach is to create a new branch within the Databricks workspace. The developer can then make changes in this new branch, commit those changes, and push the new branch to the remote Git repository. This workflow allows for isolated development without affecting the main branch, enabling the developer to propose changes via a pull request from the new branch to the main branch in the remote repository. This method adheres to common Git collaboration workflows, fostering code review and collaboration while ensuring the integrity of the main branch.

NEW QUESTION # 27
The data governance team is reviewing user for deleting records for compliance with GDPR. The following logic has been implemented to propagate deleted requests from the user_lookup table to the user aggregate table.

Assuming that user_id is a unique identifying key and that all users have requested deletion have been removed from the user_lookup table, which statement describes whether successfully executing the above logic guarantees that the records to be deleted from the user_aggregates table are no longer accessible and why?

A. No; the change data feed only tracks inserts and updates not deleted records.
B. No; files containing deleted records may still be accessible with time travel until a BACUM command is used to remove invalidated data files.
C. No; the Delta Lake DELETE command only provides ACID guarantees when combined with the MERGE INTO command
D. Yes; the change data feed uses foreign keys to ensure delete consistency throughout the Lakehouse.
E. Yes; Delta Lake ACID guarantees provide assurance that the DELETE command successed fully and permanently purged these records.

Answer: B

Explanation:
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from Explanation:
The DELETE operation in Delta Lake is ACID compliant, which means that once the operation is successful, the records are logically removed from the table. However, the underlying files that contained these records may still exist and be accessible via time travel to older versions of the table. To ensure that these records are physically removed and compliance with GDPR is maintained, a VACUUM command should be used to clean up these data files after a certain retention period. The VACUUM command will remove the files from the storage layer, and after this, the records will no longer be accessible.

NEW QUESTION # 28
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