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Cloudera CDP-3002 Exam Syllabus Topics:

Cloudera CDP Data Engineer - Certification Sample Questions:

Question 1

When analyzing an Explain Plan, what does the presence of a large number of "Nested Loop Joins" indicate about a query's potential performance?

A. It implies that the data is perfectly partitioned for the query
B. It may indicate potential performance issues for large datasets due to the high computational cost
C. It suggests that the query is fully optimized for distributed execution
D. It guarantees optimal performance for all dataset sizes

Question 2

Your Iceberg table has a hidden partition by month(event_timestamp). You frequently query with filters on the event_timestamp column. What potential problem might you encounter, and how would you address it?

A. Errors due to incorrect partition discovery; you'll need to manually update Iceberg table metadata.
B. No problems; hidden partitioning is designed for this use case.
C. Performance issues due to unnecessary file scanning; consider adding event_timestamp as an explicit partition.
D. Compatibility issues with older Spark versions; ensure you're using a version supporting hidden partitioning

Question 3

Discuss the trade-offs between using wide tables (many columns) and narrow tables (few columns) in Spark and the implications for data processing efficiency.

A. Wide tables require more storage and may lead to performance overhead due to data serialization.
B. Spark can optimize data access patterns for both wide and narrow tables equally.
C. Wide tables offer better performance for filtering operations, while narrow tables excel in aggregations.
D. Both A and C.

Question 4

What is the primary purpose of the Airflow Scheduler in Apache Airflow?

A. To monitor the state of tasks and trigger them based on their dependencies
B. To distribute tasks across workers for execution
C. To execute the code for each task in a DAG
D. To provide a user interface for monitoring and managing DAGs

Question 5

In the context of improving join performance in Spark, what does "salting" involve?

A. Physically co-locating related data on the same node
B. Seasoning the data with random noise for privacy
C. Adding a random prefix to keys to distribute skewed data more evenly
D. Encrypting the data before performing a join

Solutions: