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

Databricks Certified Data Engineer Professional Sample Questions:

1. 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) Ingesting data from Apache Kafka topics with sub-second processing requirements for immediate alerting.
C) Precomputing complex aggregations and joins from multiple large tables to accelerate BI dashboard performance.
D) Implementing a CDC (Change Data Capture) pipeline that needs to detect and respond to database changes within seconds.

2. A data engineer is tasked with building a nightly batch ETL pipeline that processes very large volumes of raw JSON logs from a data lake into Delta tables for reporting. The data arrives in bulk once per day, and the pipeline takes several hours to complete. Cost efficiency is important, but performance and reliability of completing the pipeline are the highest priorities. Which type of Databricks cluster should the data engineer configure?

A) A job cluster configured to autoscale across multiple workers during the pipeline run.
B) An all-purpose cluster always kept running to ensure low-latency job startup times.
C) A lightweight single-node cluster with low worker node count to reduce costs.
D) A high-concurrency cluster designed for interactive SQL workloads.

3. 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:

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

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

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

5. Why are Pandas UDFs often preferred over traditional PySpark UDFs in performance-critical applications involving large datasets?

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

Solutions: