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Cloudera CDP Data Engineer - Certification Sample Questions:

1. How does Spark handle data shuffling during distributed processing?

A) Spark doesn't perform data shuffling
B) By storing all data on a single node
C) By transferring only required data between executors
D) By broadcasting all data to each executor

2. In a Kubernetes environment, why is it beneficial to run the Spark Driver in its own pod?

A) To isolate the Driver from Executor pods for security reasons.
B) To use a different programming language for the Driver.
C) To automatically scale the Driver based on workload.
D) To reduce the cost of cloud resources.

3. Which of the following best describes the benefit of partition pruning in Spark SQL?

A) It decreases the amount of data shuffled during join operations.
B) It dynamically adjusts partition sizes based on the query execution plan.
C) It increases the number of partitions to maximize parallelism.
D) It selectively processes partitions that meet certain criteria, reducing 1/0.

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

5. How can you use Apache Airflow to ensure a data quality check stops the workflow if it fails, without failing subsequent tasks that are not dependent on the data quality check?

A) Set trigger_rule='all_done' on subsequent tasks.
B) Use the TaskGroup with trigger_rule='one_failed' for the data quality check task.
C) Use the BranchPythonOperator to split the workflow conditionally.
D) Use the ShortCircuitOperator with a condition that returns False if the check fails.

Solutions: