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Cloudera CDP-3002 Exam Syllabus Topics:

Cloudera CDP Data Engineer - Certification Sample Questions:

1. You encounter an error during a data quality check within your Airflow DAG. How can you access detailed information about the error to aid in troubleshooting?

A) The Airflow logs (scheduler, worker) might contain relevant error messages from the failing task.
B) The Airflow web UI task details page might display error messages related to the failed check.
C) You can directly inspect the custom Python script used for the check to identify potential issues.
D) All of the above

2. Your Spark application encounters frequent failures during the execution of a specific stage. How can you utilize Spark's persistence mechanisms to improve the application's fault tolerance?

A) Increase the number of Spark executors to handle potential resource limitations
B) Implement custom retry logic within the failing stage
C) Leverage Spark's checkpointing functionality to save application state periodically
D) Persist the RDDs used in the failing stage with MEMORY_ONLY storage level

3. You are working with a large dataset in PySpark and notice that certain operations are being executed repeatedly, leading to performance issues. Which of the following approaches should you adopt to optimize these operations?

A) Use the 'df.persist()' method.
B) Use the method.
C) Write the data to a CSV file and then read it back.
D) Increase the number of partitions using 'df.repartition()'.

4. When tuning Spark applications, why is it important to adjust the spark.executor.cores configuration?

A) To determine the number of partitions created during shuffle operations
B) To limit the maximum size of Spark's internal data structures
C) To directly control the amount of memory available to each executor
D) To specify the number of tasks that can be run in parallel on a single executor

5. You are deploying a Spark application on Kubernetes and need to specify the amount of memory allocated to each Executor. In your PySpark code, which configuration setting will you use?

A) 'spark.executor.memory'
B) 'spark.executor.instances'
C) 'spark.driver.memory'
D) 'spark.executor.memoryoverhead'

Solutions: