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Cloudera CDP Data Engineer - Certification Sample Questions:

1. In the context of Cloudera's Optimization Framework, what is the purpose of dynamic partition pruning?

A) To partition data dynamically based on query execution plans
B) To dynamically eliminate unnecessary partitions from a query plan based on runtime statistics
C) To update partition metadata in real-time
D) To increase the size of partitions dynamically based on data volume

2. Your Iceberg table experiences frequent schema evolution, with new columns being added regularly. Which of the following might you need to adjust to maintain optimal performance?

A) Spark shuffle partitions (spark.sql.shuffle.partitions)
B) Iceberg metadata table pruning frequency
C) Kubernetes resource requests for Spark executors
D) The Iceberg table's write format (vl or v2)

3. You're working with a complex DataFrame containing nested structures (e.g., arrays of structs). How can you access and manipulate data within these nested structures?

A) Directly access elements using their position within the nested structure
B) Leverage Spark SQL's built-in functions like explode and struct
C) Convert the nested data into a simpler format like a single-level DataFrame
D) Implement custom recursive functions to navigate through the nested structure

4. You're working with a team of data engineers who collaborate on developing and maintaining Airflow DAGs. How can you ensure version control and maintain a consistent development workflow?

A) Utilize a version control system like Git to track changes in the DAG code and enable collaborative development.
B) Implement a custom workflow management system outside of Airflow for managing DAG development.
C) Grant full access to the Airflow web Ul to all team members for code modifications, regardless of their roles.
D) Store all DAG code in a shared folder on a local file system, relying on manual coordination between team members.

5. You're working with an Airflow DAG that performs data quality checks on sensitive dat a. How can you ensure data security during the checks?

A) Implement data masking techniques to obfuscate sensitive information during the checks.
B) Utilize environment variables to store sensitive data and access them within the PythonOperator.
C) Store sensitive data like thresholds and comparison values directly within the DAG code.
D) All of the above

Solutions: