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Cloudera CDP-3002 Exam Syllabus Topics:

Cloudera CDP Data Engineer - Certification Sample Questions:

Question 1

What is a primary consideration when deciding to cache data in a distributed computing environment like Apache Spark?

A. Caching every dataset regardless of its access frequency
B. The trade-off between memory usage and computational efficiency
C. Ensuring data is encrypted before caching
D. Using disk storage for all cached data to improve fault tolerance

Question 2

You're deploying your Airflow DAGs to a production environment. What are some best practices to ensure reliability and maintainability?

A. Implement version control for your DAG code and configuration files to track changes and facilitate rollbacks.
B. Grant full access to the Airflow web UI to everyone for monitoring and troubleshooting purposes.
C. Use the same Airflow environment for development, testing, and production deployments.
D. Schedule DAGs to run as frequently as possible to ensure real-time data processing.

Question 3

You're building a complex Airflow DAG with numerous tasks and dependencies. How can you improve the DAG's readability and maintainability?

A. All of the above
B. Use clear and descriptive names for tasks, operators, and variables throughout the DAG code.
C. Break down the DAG into smaller sub-DAGs with well-defined functionalities.
D. Implement extensive logging within each task to capture detailed execution information.

Question 4

How can you dynamically generate tasks in Apache Airflow?

A. By defining a Python function inside the DAG file
B. By using the Variable feature to store dynamic configurations
C. By using Python loops to create tasks during DAG parsing
D. By using the Jinja templating engine

Question 5

You're tasked with optimizing an existing Airflow DAG that processes large datasets daily. The DAG has multiple tasks, some of which frequently fail due to memory constraints on the worker nodes. Which approach would best mitigate this issue without upgrading hardware?

A. Use the Pool feature to limit the number of concurrent memory-intensive tasks.
B. Implement the Retry operator to automatically retry failed tasks.
C. Split the DAG into multiple smaller DAGs to reduce the load on workers.
D. Increase the priority_weight of memory-intensive tasks to ensure they run first.

Solutions: