Free Databricks Databricks-Certified-Professional-Data-Engineer Practice Test & Real Exam Questions, Page 1

Question #1

A data engineering team needs to implement a tagging system for their tables as part of an automated ETL process, and needs to apply tags programmatically to tables in Unity Catalog.
Which SQL command adds tags to a table programmatically?

A. APPLY TAGS ON table_name VALUES ( ' key1 ' = ' value1 ' , ' key2 ' = ' value2 ' );

B. COMMENT ON TABLE table_name TAGS ( ' key1 ' = ' value1 ' , ' key2 ' = ' value2 ' );

C. ALTER TABLE table_name SET TAGS ( ' key1 ' = ' value1 ' , ' key2 ' = ' value2 ' );

D. SET TAGS FOR table_name AS ( ' key1 ' = ' value1 ' , ' key2 ' = ' value2 ' );

Discussion 0

Correct Answer: C Vote an answer

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Question #2

A workspace admin has created a new catalog called finance_data and wants to delegate permission management to a finance team lead without giving them full admin rights.
Which privilege should be granted to the finance team lead?

A. GRANT OPTION privilege on the finance_data catalog.

B. Make the finance team lead a metastore admin.

C. ALL PRIVILEGES on the finance_data catalog.

D. MANAGE privilege on the finance_data catalog.

Discussion 0

Correct Answer: D Vote an answer

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Question #3

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 new branch commit all changes and push changes to the remote Git repertory.

B. Use Repos to merge all differences and make a pull request back to the remote repository.

C. Use repos to create a fork of the remote repository commit all changes and make a pull request on the source repository

D. Use repos to merge all difference and make a pull request back to the remote repository.

Discussion 0

Correct Answer: A Vote an answer

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Question #4

A data engineer, while designing a Pandas UDF to process financial time-series data with complex calculations that require maintaining state across rows within each stock symbol group, must ensure the function is efficient and scalable.
Which approach will solve the problem with minimum overhead while preserving data integrity?

A. Use applyInPandas() on a Spark DataFrame that receives all rows for each stock symbol as a Pandas DataFrame, allowing processing within each group while maintaining state variables local to each group's processing function.

B. Use a grouped_agg Pandas UDF that processes each stock symbol group independently, maintaining state through intermediate aggregation results that get passed between successive UDF calls via broadcast variables.

C. Use a SCALAR Pandas UDF that processes the entire dataset at once, implementing custom partitioning logic within the UDF to group by stock symbol and maintain state using global variables shared across all executor processes.

D. Use a SCALAR_ITER Pandas UDF with iterator-based processing, implementing state management through persistent storage (Delta tables) that gets updated after each batch to maintain continuity across iterator chunks.

Discussion 0

Correct Answer: A Vote an answer

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Question #5

A junior member of the data engineering team is exploring the language interoperability of Databricks notebooks. The intended outcome of the below code is to register a view of all sales that occurred in countries on the continent of Africa that appear in the geo_lookup table.
Before executing the code, running SHOW TABLES on the current database indicates the database contains only two tables: geo_lookup and sales .

Which statement correctly describes the outcome of executing these command cells in order in an interactive notebook?

A. Cmd 1 will succeed and Cmd 2 will fail, countries at will be a Python variable containing a list of strings.

B. Both commands will succeed. Executing show tables will show that countries at and sales at have been registered as views.

C. Cmd 1 will succeed and Cmd 2 will fail, countries at will be a Python variable representing a PySpark DataFrame.

D. Both commands will fail. No new variables, tables, or views will be created.

E. Cmd 1 will succeed. Cmd 2 will search all accessible databases for a table or view named countries af:
if this entity exists, Cmd 2 will succeed.

Discussion 0

Correct Answer: A Vote an answer

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Question #6

An organization processes customer data from web and mobile applications. Data includes names, emails, phone numbers, and location history. Data arrives both as batch files (from SFTP daily) and streaming JSON events (from Kafka in real-time).
To comply with data privacy policies, the following requirements must be met:
* Personally Identifiable Information (PII) such as email, phone number, and IP address must be masked or anonymized before storage.
* Both batch and streaming pipelines must apply consistent PII handling.
* Masking logic must be auditable and reproducible.
* The masked data must remain usable for downstream analytics.
How should the data engineer design a compliant data pipeline on Databricks that supports both batch and streaming modes, applies data masking to PII, and maintains traceability for audits?

A. Ingest both batch and streaming data using Lakeflow Declarative Pipelines, and apply masking via Unity Catalog column masks at read time to avoid modifying the data during ingestion.

B. Load batch data with notebooks and ingest streaming data with SQL Warehouses; use Unity Catalog column masks on Silver tables to redact fields after storage.

C. Use Lakeflow Declarative Pipelines for batch and streaming ingestion, define a PII masking function
, and apply it during Bronze ingestion before writing to Delta Lake .

D. Allow PII to be stored unmasked in Bronze for lineage tracking, then apply masking logic in Gold tables used for reporting.

Discussion 0

Correct Answer: C Vote an answer

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Question #7

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.

Discussion 0

Correct Answer: A Vote an answer

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Question #8

Which statement describes the default execution mode for Databricks Auto Loader?

A. Cloud vendor-specific queue storage and notification services are configured to track newly arriving files; new files are incrementally and impotently into the target Delta Lake table.

B. Webhook trigger Databricks job to run anytime new data arrives in a source directory; new data automatically merged into target tables using rules inferred from the data.

C. New files are identified by listing the input directory; new files are incrementally and idempotently loaded into the target Delta Lake table.

D. New files are identified by listing the input directory; the target table is materialized by directory querying all valid files in the source directory.

Discussion 0

Correct Answer: C Vote an answer

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Question #9

A data engineer wants to reflector the following DLT code, which includes multiple definition with very similar code:

In an attempt to programmatically create these tables using a parameterized table definition, the data engineer writes the following code.

The pipeline runs an update with this refactored code, but generates a different DAG showing incorrect configuration values for tables.
How can the data engineer fix this?

A. Wrap the loop inside another table definition, using generalized names and properties to replace with those from the inner table

B. Convert the list of configuration values to a dictionary of table settings, using table names as keys.

C. Load the configuration values for these tables from a separate file, located at a path provided by a pipeline parameter.

D. Convert the list of configuration values to a dictionary of table settings, using different input the for loop.

Discussion 0

Correct Answer: B Vote an answer

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Question #10

Which statement regarding stream-static joins and static Delta tables is correct?

A. Each microbatch of a stream-static join will use the most recent version of the static Delta table as of the job ' s initialization.

B. The checkpoint directory will be used to track state information for the unique keys present in the join.

C. Each microbatch of a stream-static join will use the most recent version of the static Delta table as of each microbatch.

D. Stream-static joins cannot use static Delta tables because of consistency issues.

E. The checkpoint directory will be used to track updates to the static Delta table.

Discussion 0

Correct Answer: C Vote an answer

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Question #11

Which distribution does Databricks support for installing custom Python code packages?

A. CRAM

B. sbt

C. Wheels

D. nom

E. jars

F. CRAN

Discussion 0

Correct Answer: D Vote an answer

Question #12

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 task A fails during a scheduled run, which statement describes the results of this run?

A. Unless all tasks complete successfully, no changes will be committed to the Lakehouse; because task A failed, all commits will be rolled back automatically.

B. Tasks B and C will attempt to run as configured; any changes made in task A will be rolled back due to task failure.

C. Tasks B and C will be skipped; task A will not commit any changes because of stage failure.

D. Because all tasks are managed as a dependency graph, no changes will be committed to the Lakehouse until all tasks have successfully been completed.

E. Tasks B and C will be skipped; some logic expressed in task A may have been committed before task failure.

Discussion 0

Correct Answer: E Vote an answer

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Question #13

A query is taking too long to run. After investigating the Spark UI, the data engineer discovered a significant amount of disk spill . The compute instance being used has a core-to-memory ratio of 1:2.
What are the two steps the data engineer should take to minimize spillage? (Choose 2 answers)

A. Increase spark.sql.files.maxPartitionBytes.

B. Choose a compute instance with more disk space.

C. Reduce spark.sql.files.maxPartitionBytes.

D. Choose a compute instance with more network bandwidth.

E. Choose a compute instance with a higher core-to-memory ratio.

Discussion 0

Correct Answer: C,E Vote an answer

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Free Databricks Databricks-Certified-Professional-Data-Engineer Practice Test & Real Exam Questions

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