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Databricks-Certified-Professional-Data-Engineer certification exam is a comprehensive test that covers all aspects of data engineering with Databricks. Databricks-Certified-Professional-Data-Engineer Exam is designed to test the candidate's knowledge of Databricks architecture, data engineering concepts, data processing with Databricks, and data storage with Databricks. Databricks-Certified-Professional-Data-Engineer exam also tests the candidate's ability to design, implement, and maintain data engineering solutions using Databricks.

Databricks Certified Professional Data Engineer is a certification exam that measures individuals' knowledge and skills in using Databricks to manipulate big data. Databricks is a cloud-based data processing platform that allows data engineers to build, deploy, and manage big data processing pipelines. Databricks Certified Professional Data Engineer Exam certification exam is designed to validate the expertise of data engineers who work with Databricks.

Databricks Certified Professional Data Engineer Exam Sample Questions (Q122-Q127):

NEW QUESTION # 122
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. Yes: Delta Lake ACID guarantees provide assurance that the DELETE command successed fully and permanently purged these records.
C. No: files containing deleted records may still be accessible with time travel until a BACUM command is used to remove invalidated data files.
D. No: the Delta Lake DELETE command only provides ACID guarantees when combined with the MERGE INTO command

Answer: C

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 # 123
A data ingestion task requires a one-TB JSON dataset to be written out to Parquet with a target part-file size of 512 MB. Because Parquet is being used instead of Delta Lake, built-in file-sizing features such as Auto-Optimize & Auto-Compaction cannot be used.
Which strategy will yield the best performance without shuffling data?

A. Set spark.sql.shuffle.partitions to 512, ingest the data, execute the narrow transformations, and then write to parquet.
B. Set spark.sql.shuffle.partitions to 2,048 partitions (1TB*1024*1024/512), ingest the data, execute the narrow transformations, optimize the data by sorting it (which automatically repartitions the data), and then write to parquet.
C. Set spark.sql.files.maxPartitionBytes to 512 MB, ingest the data, execute the narrow transformations, and then write to parquet.
D. Ingest the data, execute the narrow transformations, repartition to 2,048 partitions (1TB* 1024*1024/512), and then write to parquet.
E. Set spark.sql.adaptive.advisoryPartitionSizeInBytes to 512 MB bytes, ingest the data, execute the narrow transformations, coalesce to 2,048 partitions (1TB*1024*1024/512), and then write to parquet.

Answer: B

NEW QUESTION # 124
A table nameduser_ltvis being used to create a view that will be used by data analysts on various teams.
Users in the workspace are configured into groups, which are used for setting up data access using ACLs.
Theuser_ltvtable has the following schema:
email STRING, age INT, ltv INT
The following view definition is executed:

An analyst who is not a member of the marketing group executes the following query:
SELECT * FROM email_ltv
Which statement describes the results returned by this query?

A. Only the email and ltv columns will be returned; the email column will contain the string
"REDACTED" in each row.
B. The email, age. and ltv columns will be returned with the values in user ltv.
C. Only the email and itv columns will be returned; the email column will contain all null values.
D. The email and ltv columns will be returned with the values in user itv.
E. Three columns will be returned, but one column will be named "redacted" and contain only null values.

Answer: A

Explanation:
The code creates a view called email_ltv that selects the email and ltv columns from a table called user_ltv, which has the following schema: email STRING, age INT, ltv INT. The code also uses the CASE WHEN expression to replace the email values with the string "REDACTED" if the user is not a member of the marketing group. The user who executes the query is not a member of the marketing group, so they will only see the email and ltv columns, and the email column will contain the string "REDACTED" in each row.
Verified References: [Databricks Certified Data Engineer Professional], under "Lakehouse" section; Databricks Documentation, under "CASE expression" section.

NEW QUESTION # 125
Which of the following locations in the Databricks product architecture hosts the notebooks and jobs?

A. Databricks web application
B. Control plane
C. Data plane
D. JDBC data source
E. Databricks Filesystem

Answer: B

Explanation:
Explanation
The answer is Control Pane,
Databricks operates most of its services out of a control plane and a data plane, please note serverless features like SQL Endpoint and DLT compute use shared compute in Control pane.
Control Plane: Stored in Databricks Cloud Account
*The control plane includes the backend services that Databricks manages in its own Azure account. Notebook commands and many other workspace configurations are stored in the control plane and encrypted at rest.
Data Plane: Stored in Customer Cloud Account
*The data plane is managed by your Azure account and is where your data resides. This is also where data is processed. You can use Azure Databricks connectors so that your clusters can connect to external data sources outside of your Azure account to ingest data or for storage.
Timeline Description automatically generated

NEW QUESTION # 126
Which statement describes integration testing?

A. Requires an automated testing framework
B. Validates interactions between subsystems of your application
C. Validates an application use case
D. Validates behavior of individual elements of your application
E. Requires manual intervention

Answer: B

Explanation:
This is the correct answer because it describes integration testing. Integration testing is a type of testing that validates interactions between subsystems of your application, such as modules, components, or services.
Integration testing ensures that the subsystems work together as expected and produce the correct outputs or results. Integration testing can be done at different levels of granularity, such as component integration testing, system integration testing, or end-to-end testing. Integration testing can help detect errors or bugs that may not be found by unit testing, which only validates behavior of individual elements of your application.
Verified References: [Databricks Certified Data Engineer Professional], under "Testing" section; Databricks Documentation, under "Integration testing" section.

NEW QUESTION # 127
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