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Databricks Databricks-Certified-Data-Engineer-Professional Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Modeling and Storage	20%	- File Formats - Storage Optimization - Data Modeling
Topic 2: Monitoring and Troubleshooting	16%	- Troubleshooting - Monitoring - Performance Optimization
Topic 3: Data Processing	28%	- Spark SQL - Structured Streaming - Data Transformation - ETL Pipelines
Topic 4: Databricks Lakehouse Platform	24%	- Unity Catalog - Data Management - Delta Lake - Lakehouse Architecture
Topic 5: Data Quality and Governance	12%	- Data Quality - Data Lineage - Governance

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q26-Q31):

NEW QUESTION # 26
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. When configuring an external data warehouse for all table storage. leverage Databricks for all ELT.
B. When the workspace is being configured, make sure that external cloud object storage has been mounted.
C. 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
D. When tables are created, make sure that the external keyword is used in the create table statement.
E. Whenever a table is being created, make sure that the location keyword is used.

Answer: E

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 # 27
The business intelligence team has a dashboard configured to track various summary metrics for retail stories. This includes total sales for the previous day alongside totals and averages for a variety of time periods. The fields required to populate this dashboard have the following schema:

For Demand forecasting, the Lakehouse contains a validated table of all itemized sales updated incrementally in near real-time. This table named products_per_order, includes the following fields:

Because reporting on long-term sales trends is less volatile, analysts using the new dashboard only require data to be refreshed once daily. Because the dashboard will be queried interactively by many users throughout a normal business day, it should return results quickly and reduce total compute associated with each materialization.
Which solution meets the expectations of the end users while controlling and limiting possible costs?

A. Use Structure Streaming to configure a live dashboard against the products_per_order table within a Databricks notebook.
B. Define a view against the products_per_order table and define the dashboard against this view.
C. Configure a webhook to execute an incremental read against products_per_order each time the dashboard is refreshed.
D. Use the Delta Cache to persists the products_per_order table in memory to quickly the dashboard with each query.
E. Populate the dashboard by configuring a nightly batch job to save the required values as a table overwritten with each update.

Answer: E

NEW QUESTION # 28
A Delta Lake table was created with the below query:

Consider the following query:
DROP TABLE prod.sales_by_store
If this statement is executed by a workspace admin, which result will occur?

A. Nothing will occur until a COMMIT command is executed.
B. Data will be marked as deleted but still recoverable with Time Travel.
C. The table will be removed from the catalog but the data will remain in storage.
D. The table will be removed from the catalog and the data will be deleted.
E. An error will occur because Delta Lake prevents the deletion of production data.

Answer: D

Explanation:
When a table is dropped in Delta Lake, the table is removed from the catalog and the data is deleted. This is because Delta Lake is a transactional storage layer that provides ACID guarantees. When a table is dropped, the transaction log is updated to reflect the deletion of the table and the data is deleted from the underlying storage.

NEW QUESTION # 29
The data governance team has instituted a requirement that all tables containing Personal Identifiable Information (PH) must be clearly annotated. This includes adding column comments, table comments, and setting the custom table property "contains_pii" = true.
The following SQL DDL statement is executed to create a new table:
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from

Which command allows manual confirmation that these three requirements have been met?

A. SHOW TABLES dev
B. DESCRIBE DETAIL dev.pii test
C. DESCRIBE HISTORY dev.pii test
D. DESCRIBE EXTENDED dev.pii test
E. SHOW TBLPROPERTIES dev.pii test

Answer: D

Explanation:
This is the correct answer because it allows manual confirmation that these three requirements have been met. The requirements are that all tables containing Personal Identifiable Information (PII) must be clearly annotated, which includes adding column comments, table comments, and setting the custom table property "contains_pii" = true. The DESCRIBE EXTENDED command is used to display detailed information about a table, such as its schema, location, properties, and comments. By using this command on the dev.pii_test table, one can verify that the table has been created with the correct column comments, table comment, and custom table property as specified in the SQL DDL statement.

NEW QUESTION # 30
A data engineer wants to enforce the principle of least privilege when configuring ACLs for Databricks jobs in a collaborative workspace. Which approach should the data engineer use?

A. Assign users only the minimum permission level (e.g., CAN RUN or CAN VIEW) required for their role on each job.
B. Grant CAN RUN permission to everyone and CAN MANAGE to a single admin group.
C. Grant all users CAN MANAGE permission on all jobs to avoid access issues.
D. Use only folder-level permissions and avoid setting permissions on individual jobs.

Answer: A

Explanation:
Assigning the minimum required permission level on each job ensures users can perform only the actions necessary for their role. This directly enforces the principle of least privilege while maintaining secure and controlled access in a collaborative workspace.

NEW QUESTION # 31
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