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

Section	Objectives
Data Modeling and Transformation	- Dimensional modeling concepts - Performance optimization techniques - Spark SQL transformations
Delta Lake and Data Management	- Time travel and versioning - Delta Lake transactions and ACID properties - Schema evolution and enforcement
Production Pipelines and Orchestration	- Error handling and recovery strategies - Databricks Workflows - Job scheduling and monitoring
Data Ingestion and Processing	- Batch and streaming ingestion with Auto Loader - Structured Streaming fundamentals - ETL pipeline design patterns
Databricks Lakehouse Platform Architecture	- Workspace and cluster architecture - Medallion architecture (Bronze, Silver, Gold) - Data governance concepts (Unity Catalog basics)

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q109-Q114):

NEW QUESTION # 109
An upstream system is emitting change data capture (CDC) logs that are being written to a cloud object storage directory. Each record in the log indicates the change type (insert, update, or delete) and the values for each field after the change. The source table has a primary key identified by the field pk_id.
For auditing purposes, the data governance team wishes to maintain a full record of all values that have ever been valid in the source system. For analytical purposes, only the most recent value for each record needs to be recorded. The Databricks job to ingest these records occurs once per hour, but each individual record may have changed multiple times over the course of an hour.
Which solution meets these requirements?

A. Use Delta Lake's change data feed to automatically process CDC data from an external system, propagating all changes to all dependent tables in the Lakehouse.
B. Create a separate history table for each pk_id resolve the current state of the table by running a Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from union all filtering the history tables for the most recent state.
C. Ingest all log information into a bronze table; use merge into to insert, update, or delete the most recent entry for each pk_id into a silver table to recreate the current table state.
D. Iterate through an ordered set of changes to the table, applying each in turn; rely on Delta Lake's versioning ability to create an audit log.
E. Use merge into to insert, update, or delete the most recent entry for each pk_id into a bronze table, then propagate all changes throughout the system.

Answer: C

Explanation:
CDF captures changes only from a Delta table and is only forward-looking once enabled. The CDC logs are writing to object storage. So you would need to ingestion those and merge into downstream tables.

NEW QUESTION # 110
All records from an Apache Kafka producer are being ingested into a single Delta Lake table with the following schema:
key BINARY, value BINARY, topic STRING, partition LONG, offset LONG, timestamp LONG There are 5 unique topics being ingested. Only the "registration" topic contains Personal Identifiable Information (PII). The company wishes to restrict access to PII. The company also wishes to only retain records containing PII in this table for 14 days after initial ingestion.
However, for non-PII information, it would like to retain these records indefinitely.
Which of the following solutions meets the requirements?

A. Data should be partitioned by the registration field, allowing ACLs and delete statements to be set for the PII directory.
B. Because the value field is stored as binary data, this information is not considered PII and no special precautions should be taken.
C. All data should be deleted biweekly; Delta Lake's time travel functionality should be leveraged to maintain a history of non-PII information.
D. Separate object storage containers should be specified based on the partition field, allowing isolation at the storage level.
E. Data should be partitioned by the topic field, allowing ACLs and delete statements to leverage partition boundaries.

Answer: E

Explanation:
By default partitionning by a column will create a separate folder for each subset data linked to the partition.

NEW QUESTION # 111
A data engineer needs to implement column masking for a sensitive column in a Unity Catalog- managed table. The masking logic must dynamically check if users belong to specific groups defined in a separate table (group_access) that maps groups to allowed departments. Which approach should the engineer use to efficiently enforce this requirement?

A. Create a view without selecting the sensitive column.
B. Apply a column mask that references the group_access mapping table in its UDF.
C. Use a row filter to restrict access based on the user's group.
D. Create a UDF that hardcodes allowed groups and apply it as a column mask.

Answer: B

Explanation:
Databricks Unity Catalog supports dynamic column masking, where masking logic can be implemented using SQL functions or UDFs that reference external mapping tables or metadata for context-aware access control.
By referencing the group_access table inside the masking function, the mask dynamically evaluates whether a requesting user belongs to an authorized group. If permitted, the original column value is returned; otherwise, a masked value (such as NULL or asterisks) is shown.
This method enables fine-grained, data-driven masking policies while maintaining a single authoritative access mapping source.
Hardcoding values (A) reduces flexibility, and row filters (D) apply to entire rows rather than specific columns. Therefore, C correctly aligns with Databricks best practices for dynamic masking.

NEW QUESTION # 112
Why are Pandas UDFs often preferred over traditional PySpark UDFs in performance-critical applications involving large datasets?

A. They minimize memory usage by streaming each row individually through a lightweight Python wrapper, avoiding batch processing overhead.
B. They leverage Apache Arrow to enable vectorized operations between the JVM and Python runtimes, reducing serialization costs and improving computational efficiency.
C. They allow row-level execution of functions in Python with native Spark optimization, removing the need for columnar execution.
D. They eliminate the JVM-Python boundary by bypassing serialization entirely, thereby avoiding data conversion overhead.

Answer: B

Explanation:
Pandas UDFs use Apache Arrow to transfer data between the JVM and Python in a columnar, vectorized format. This significantly reduces serialization overhead and enables efficient batch processing, resulting in much better performance than traditional row-by-row PySpark UDFs on large datasets.

NEW QUESTION # 113
Review the following error traceback:

Which statement describes the error being raised?

A. There is a type error because a column object cannot be multiplied.
B. There is no column in the table named heartrateheartrateheartrate
C. There is a type error because a DataFrame object cannot be multiplied.
D. There is a syntax error because the heartrate column is not correctly identified as a column.
E. The code executed was PvSoark but was executed in a Scala notebook.

Answer: B

Explanation:
https://sparkbyexamples.com/spark/spark-cannot-resolve-given-input-columns/

NEW QUESTION # 114
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