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Databricks Certified Professional Data Engineer Exam Sample Questions (Q145-Q150):

NEW QUESTION # 145
if you run the command VACUUM transactions retain 0 hours? What is the outcome of this command?

A. Command will fail if you have an active transaction running
B. Command will be successful, but historical data will be removed
C. Command will fail, you cannot run the command with retentionDurationcheck enabled
D. Command runs successful and compacts all of the data in the table
E. Command will be successful, but no data is removed

Answer: C

Explanation:
Explanation
The answer is,
Command will fail, you cannot run the command with retentionDurationcheck enabled.
1.VACUUM [ [db_name.]table_name | path] [RETAIN num HOURS] [DRY RUN]
*Recursively vacuum directories associated with the Delta table and remove data files that are no longer in the latest state of the transaction log for the table and are older than a retention threshold. Default is 7 Days.
*The reason this check is enabled is because, DELTA is trying to prevent unintentional deletion of history, and also one important thing to point out is with 0 hours of retention there is a possibility of data loss(see below kb) Documentation in VACUUM https://docs.delta.io/latest/delta-utility.html
https://kb.databricks.com/delta/data-missing-vacuum-parallel-write.html

NEW QUESTION # 146
A data engineer needs to provide access to a group named manufacturing-team. The team needs privileges to create tables in the quality schema.
Which set of SQL commands will grant a group named manufacturing-team to create tables in a schema named production with the parent catalog named manufacturing with the least privileges?

A. GRANT USE TABLE ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT USE SCHEMA ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT USE CATALOG ON CATALOG manufacturing TO manufacturing-team;
B. GRANT CREATE TABLE ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT CREATE SCHEMA ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT USE CATALOG ON CATALOG manufacturing TO manufacturing-team;
C. GRANT CREATE TABLE ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT USE SCHEMA ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT USE CATALOG ON CATALOG manufacturing TO manufacturing-team;
D. GRANT CREATE TABLE ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT CREATE SCHEMA ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT CREATE CATALOG ON CATALOG manufacturing TO manufacturing-team;

Answer: C

Explanation:
To create a table within a schema, a principal must have CREATE TABLE on the schema, USE SCHEMA on that schema, and USE CATALOG on the parent catalog. This combination ensures the group has just enough privileges to create objects in that schema without excessive permissions like CREATE SCHEMA or CREATE CATALOG.
Reference Source: Databricks Unity Catalog Privilege Model - "Privileges Required to Create a Table."

NEW QUESTION # 147
A junior data engineer seeks to leverage Delta Lake's Change Data Feed functionality to create a Type 1 table representing all of the values that have ever been valid for all rows in abronzetable created with the propertydelta.enableChangeDataFeed = true. They plan to execute the following code as a daily job:

Which statement describes the execution and results of running the above query multiple times?

A. Each time the job is executed, newly updated records will be merged into the target table, overwriting previous values with the same primary keys.
B. Each time the job is executed, the target table will be overwritten using the entire history of inserted or updated records, giving the desired result.
C. Each time the job is executed, only those records that have been inserted or updated since the last execution will be appended to the target table giving the desired result.
D. Each time the job is executed, the differences between the original and current versions are calculated; this may result in duplicate entries for some records.
E. Each time the job is executed, the entire available history of inserted or updated records will be appended to the target table, resulting in many duplicate entries.

Answer: E

Explanation:
Reading table's changes, captured by CDF, using spark.read means that you are reading them as a static source. So, each time you run the query, all table's changes (starting from the specified startingVersion) will be read.

NEW QUESTION # 148
A data engineer has created a transactions Delta table on Databricks that should be used by the analytics team.
The analytics team wants to use the table with another tool that requires Apache Iceberg format.
What should the data engineer do?

A. Require the analytics team to use a tool that supports Delta table.
B. Create an Iceberg copy of the transactions Delta table which can be used by the analytics team.
C. Convert the transactions Delta table to Iceberg and enable uniform so that the table can be read as a Delta table.
D. Enable uniform on the transactions table to 'iceberg' so that the table can be read as an Iceberg table.

Answer: C

Explanation:
Delta Lake introduced Delta Universal Format (Delta UniForm), which allows seamless interoperability between Delta Lake and Apache Iceberg. This means a Delta table can be converted into an Iceberg table while maintaining Delta capabilities.
Explanation of Each Option:
* (A) Require the analytics team to use a tool that supports Delta table
* Incorrect: While Delta Lake is widely used, requiring the team to change tools is not a flexible or scalable solution.
* (B) Enable uniform on the transactions table to 'iceberg' so that the table can be read as an Iceberg table
* Incorrect:
* The uniform feature must be enabled after conversion.
* You cannot directly enable uniform without first converting the table.
* (C) Create an Iceberg copy of the transactions Delta table which can be used by the analytics team
* Incorrect:
* Creating a separate Iceberg copy would duplicate storage and increase maintenance complexity.
* This is not necessary when Delta UniForm allows direct compatibility with Iceberg.
* (D) Convert the transactions Delta table to Iceberg and enable uniform so that the table can be read as a Delta table
* Correct:
* The best approach is to convert the existing Delta table to Iceberg using the Databricks Delta to Iceberg migration tools.
* After conversion, enabling uniform ensures the table remains accessible in both Delta and Iceberg formats.
Conclusion:
The best practice for interoperability between Delta and Iceberg is to convert the Delta table to Iceberg and enable uniform, ensuring cross-compatibility without data duplication.
Thus, Option (D) is the correct answer.
References:
Delta UniForm for Apache Iceberg - Databricks Documentation
Convert Delta to Iceberg - Databricks

NEW QUESTION # 149
A data architect is designing a Databricks solution to efficiently process data for different business requirements.
In which scenario should a data engineer use a materialized view compared to a streaming table?

A. Processing high-volume, continuous clickstream data from a website to monitor user behavior in real-time.
B. Implementing a CDC (Change Data Capture) pipeline that needs to detect and respond to database changes within seconds.
C. Precomputing complex aggregations and joins from multiple large tables to accelerate BI dashboard performance.
D. Ingesting data from Apache Kafka topics with sub-second processing requirements for immediate alerting.

Answer: C

Explanation:
Comprehensive and Detailed Explanation From Exact Extract of Databricks Data Engineer Documents:
Materialized views in Databricks are optimized for precomputing and caching results of complex SQL queries, joins, and aggregations. They store query outputs physically and automatically refresh on a schedule or incremental change basis, drastically improving BI dashboard performance and reducing compute costs.
Conversely, streaming tables are designed for real-time data ingestion and processing, enabling event-driven analytics and low-latency use cases.
Databricks documentation explicitly recommends materialized views for analytical workloads with periodic updates and streaming tables for continuously updating sources. Therefore, the correct choice is C, where complex aggregations from large tables benefit most from materialized precomputation for fast reporting.

NEW QUESTION # 150
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Databricks Certified Professional Data Engineer Exam Sample Questions (Q145-Q150):

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