Cloudera CDP-3002 : CDP Data Engineer - Certification Exam

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Cloudera CDP-3002 Exam Syllabus Topics:

Cloudera CDP Data Engineer - Certification Sample Questions:

Question 1

You're working with a large dataset that needs to be partitioned and processed in chunks to improve efficiency. How can you achieve this using Airflow operators?

A. Leverage the File transform operator to partition the data based on specific criteria within the operator itself.
B. Use the Split Operator to divide the data into smaller subsets and chain them with downstream processing tasks.
C. Configure the source system to provide the data pre-partitioned for efficient processing.
D. Implement a custom Python script to handle partitioning and then use the BashOperator to execute the script within the DAG.

Question 2

In Apache Airflow, how does the "catchup" parameter in a DAG definition influence the scheduling of DAG runs when backfilling data?

A. It limits the number of backfilled DAG runs to the most recent execution interval only.
B. It enables backfilling, causing Airflow to schedule DAG runs for all intervals between the DAG's start_date and the current date.
C. It disables backfilling entirely, ensuring that only future DAG runs are executed.
D. It forces Airflow to ignore the start_date and schedule the DAG run immediately.

Question 3

You're building an Airflow DAG that consists of multiple interdependent ETL pipelines. How can you ensure they execute in the correct order and avoid conflicts?

A. Utilize Airflow sub-DAGs to group related tasks and define dependencies between them.
B. Run all pipelines simultaneously, assuming they are independent.
C. Implement a custom script to manage the execution order of the pipelines.
D. Schedule each pipeline separately with appropriate scheduling intervals.

Question 4

You need to design a DAG that can be easily triggered based on external events or data availability. How can you achieve this functionality?

A. Utilize Airflow sensors like FileSensor or S3KeySensor to wait for specific conditions before triggering the DAG.
B. Schedule the DAG to run continuously at a fixed interval.
C. Rely on manual intervention to trigger the DAG whenever necessary.
D. Implement custom code within the DAG to actively check for external events or data availability.

Question 5

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

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

Solutions: