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

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

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Databricks-Certified-Data-Engineer-Professional Practice Questions: Databricks Certified Data Engineer Professional Exam & Databricks-Certified-Data-Engineer-Professional Exam Dumps Files

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q66-Q71):

NEW QUESTION # 66
The data architect has decided that once data has been ingested from external sources into the Databricks Lakehouse, table access controls will be leveraged to manage permissions for all production tables and views.
The following logic was executed to grant privileges for interactive queries on a production database to the core engineering group.
GRANT USAGE ON DATABASE prod TO eng;
GRANT SELECT ON DATABASE prod TO eng;
Assuming these are the only privileges that have been granted to the eng group and that these users are not workspace administrators, which statement describes their privileges?

A. Group members have full permissions on the prod database and can also assign permissions to other users or groups.
B. Group members are able to create, query, and modify all tables and views in the prod database, but cannot define custom functions.
C. Group members are able to list all tables in the prod database but are not able to see the results of any queries on those tables.
D. Group members are able to query and modify all tables and views in the prod database, but cannot create new tables or views.
E. Group members are able to query all tables and views in the prod database, but cannot create or edit anything in the database.

Answer: E

Explanation:
The GRANT USAGE ON DATABASE prod TO eng command grants the eng group the permission to use the prod database, which means they can list and access the tables and views in the database. The GRANT SELECT ON DATABASE prod TO eng command grants the eng group the permission to select data from the tables and views in the prod database, which means they can query the data using SQL or DataFrame API. However, these commands do not grant the eng group any other permissions, such as creating, modifying, or deleting tables and views, or defining custom functions. Therefore, the eng group members are able to query all tables and views in the prod database, but cannot create or edit anything in the database.

NEW QUESTION # 67
A data engineer is evaluating tools to build a production-grade data pipeline. The team must process change data from cloud object storage, filter out or isolate invalid records, and ensure the timely delivery of clean data to downstream consumers. The team is small, under tight deadlines, and wants to minimize operational overhead while keeping pipelines auditable and maintainable.
Which approach should the data engineer implement?

A. Use LDP to build declarative pipelines with Streaming Tables and Materialized Views, leveraging built-in support for data expectations and incremental processing.
B. Use a hybrid approach: Ingest with Auto Loader into Bronze tables, then process using SQL queries in Databricks Workflows to generate cleaned Silver and Gold tables on a schedule.
C. Implement ingestion using Auto Loader with Structured Streaming, and manage invalid data handling and table updates using checkpointing and merge logic.
D. Ingest data directly into Delta tables via Spark jobs, apply data quality filters using UDFs, and use LDP for creating Materialized Views.

Answer: A

Explanation:
LDP provides a declarative framework for building production-grade pipelines with minimal operational overhead. Streaming Tables and Materialized Views handle incremental processing automatically, while built-in data expectations allow invalid records to be filtered or isolated in a consistent and auditable way. This approach is well suited for small teams under tight deadlines, as it simplifies maintenance, improves reliability, and ensures timely delivery of clean data to downstream consumers.

NEW QUESTION # 68
The security team is exploring whether or not the Databricks secrets module can be leveraged for connecting to an external database.
After testing the code with all Python variables being defined with strings, they upload the password to the secrets module and configure the correct permissions for the currently active Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from user. They then modify their code to the following (leaving all other variables unchanged).

Which statement describes what will happen when the above code is executed?

A. An interactive input box will appear in the notebook; if the right password is provided, the connection will succeed and the password will be printed in plain text.
B. The connection to the external table will succeed; the string "redacted" will be printed.
C. The connection to the external table will succeed; the string value of password will be printed in plain text.
D. The connection to the external table will fail; the string "redacted" will be printed.
E. An interactive input box will appear in the notebook; if the right password is provided, the connection will succeed and the encoded password will be saved to DBFS.

Answer: B

Explanation:
This is the correct answer because the code is using the dbutils.secrets.get method to retrieve the password from the secrets module and store it in a variable. The secrets module allows users to securely store and access sensitive information such as passwords, tokens, or API keys. The connection to the external table will succeed because the password variable will contain the actual password value. However, when printing the password variable, the string "redacted" will be displayed instead of the plain text password, as a security measure to prevent exposing sensitive information in notebooks.

NEW QUESTION # 69
A data engineer is optimizing a MERGE operation on an 800GB UC-managed table that experiences frequent updates and deletions. Which two actions should the engineer prioritize to improve MERGE performance? (Choose two.)

A. Use ZORDER on high-cardinality columns.
B. Enable deletion vectors on the table if not already enabled.
C. Overwrite the table instead of Merge.
D. Apply liquid clustering using the merge join keys.
E. Partition the table by date.

Answer: B,D

Explanation:
Liquid clustering on the merge join keys improves data locality and reduces the amount of data scanned during MERGE operations, which is especially effective for large, frequently updated tables. Enabling deletion vectors avoids rewriting entire Parquet files for updates and deletes, significantly reducing I/O and improving MERGE performance on Unity Catalog-managed tables.

NEW QUESTION # 70
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. Use only folder-level permissions and avoid setting permissions on individual jobs.
D. Grant all users CAN MANAGE permission on all jobs to avoid access issues.

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 # 71
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