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Cloudera CDP Data Engineer - Certification Sample Questions:

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

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

2. Your Airflow DAG includes data quality checks that involve comparing data against predefined thresholds or reference datasets. How can you handle potential failures during these checks and ensure the pipeline doesn't proceed with unreliable data?

A) Implement custom logic within the PythonOperator to raise exceptions upon failed checks and trigger downstream tasks to handle the error.
B) Configure the DAG to automatically retry failed tasks associated with the data quality checks.
C) Use the BranchPythonOperator to check the success of the data quality checks and conditionally branch the DAG execution based on the outcome.
D) All of the above

3. Which of the following strategies would NOT be recommended for managing skewed data during join operations in Spark?

A) Using a broadcast join assuming one dataset is small enough to fit into memory.
B) Applying a filter to remove outliers that cause data skewness before joining.
C) Increasing the number of partitions to distribute the skewed data more evenly.
D) Salting the keys to distribute the data more evenly across partitions.

4. An Airflow DAG designed to run a sequence of data validation checks generates a dynamic number of validation tasks based on the incoming data's characteristics. Each validation task must complete successfully before a final data processing task can begin. Which Airflow feature is most suitable for implementing this pattern?

A) A BranchPythonOperator with a follow-up Join task
B) SubDAGs
C) The Dynamic Task Mapping feature
D) The TriggerDagRunOperator

5. When using Apache Airflow to schedule quality checks, which strategy helps ensure that checks are only run on the most recent data partition?
A Use the LatestOnlyOperator to skip tasks that are not the latest in a series of executions.

A) Implement a custom PythonOperator that queries the database for the latest partition.
B) Utilize the execution_date in your SQL query to target the latest partition.
C) Apply the depends_on_past=True parameter to the quality check task.

Solutions: