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Cloudera CDP-3002 Exam Syllabus Topics:

Cloudera CDP Data Engineer - Certification Sample Questions:

Question 1

Which operator or feature in Apache Airflow can be used to dynamically adjust the schedule of data quality checks based on the volume of incoming data?

A. The Scheduler component with dynamic DAG generation
B. The BranchPythonOperator to choose between different scheduling paths
C. The ExternalTaskSensor to trigger data quality checks based on external events
D. A custom PythonOperator that modifies the DAG's schedule interval

Question 2

You encounter an error message stating "Task timed out" while running your Airflow DAG. What are some potential causes and how can you troubleshoot them?

A. All of the above
B. The DAG is running too frequently, overloading the system resources.
C. The extraction task is encountering issues connecting to the source system.
D. The data processing task is taking longer than the configured timeout for the task.

Question 3

Your Airflow DAG includes tasks that can potentially fail due to various reasons. How can you handle such failures and ensure the overall workflow continues as intended?

A. All of the above
B. Utilize Airflow XCom to share information about failed tasks with downstream tasks for alternative processing.
C. Configure the DAG to automatically retry failed tasks a specific number of times.
D. Implement custom logic within each task to handle potential errors and retry failed tasks manually.

Question 4

What is the role of a Spark driver in a distributed processing job?

A. Manages communication between executors and workers
B. Coordinates tasks across the cluster
C. Stores and processes intermediate data
D. Performs computations on individual data partitions

Question 5

You're tasked with creating a DAG in Airflow that orchestrates a complex data processing workflow. What are some key considerations for designing an effective DAG?

A. Implement extensive logging within each task for detailed information, even if it slows down execution.
B. Break down the workflow into smaller, modular tasks with clear dependencies.
C. Use a single DAG for the entire workflow, regardless of its complexity.
D. Configure the DAG to run continuously without any specific scheduling or triggering mechanism.

Solutions: