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Cloudera CDP Data Engineer - Certification Sample Questions:

1. Your team is deploying a Spark application on a Kubernetes cluster. To ensure high availability, you need to decide how many executor instances to configure. Which of the following configurations in your PySpark application would correctly set the number of executor instances?

A)

B)

C)

D)

2. In the context of Spark, what is a potential downside of indiscriminate use of data caching, especially with the MEMORY_AND DISK storage level?

A) It can decrease network traffic by reducing the need for data shuffling.
B) It may increase execution time due to overheads from frequent disk 1/0 operations.
C) It enhances data security by storing intermediate results in encrypted form.
D) It can lead to reduced fault tolerance due to reliance on in-memory storage.

3. An Airflow DAG is designed to ingest data from multiple sources, transform it, and load it into a data warehouse. The transformation step is resource-intensive and should not run during peak hours (9 AM to 5 PM). How can you configure the DAG to meet this requirement?

A) Configure the DAG's schedule interval and use the TimeDelta sensor for precise timing.
B) Utilize the BranchPythonOperator to dynamically skip the transformation task during peak hours.
C) Use the time_sensor operator to delay the transformation task until off-peak hours.
D) Set the max_active_runs parameter to limit executions during peak hours.

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

5. You have a PySpark application packaged as 'MyPySparkApp-0. I-py3-none-any.whl'. In your 'app.py', you utilize a function from an external library, 'numpy', listed in your 'requirements.txt'. How should you deploy this application to ensure 'numpy' is available at runtime?

A) Upload 'app.py' and manually install 'numpy' on all nodes before submitting using 'spark-submit app.py'.
B) Upload 'MyPySparkApp-0.1-py3-none-any.whl' only and submit using 'spark-submit --py-files MyPySparkApp-0.1-py3-none-any.whl'.
C) Upload both 'app.py' and 'MyPySparkApp-0.1-py3-none-any.whl' and submit using 'spark-submit --py-files MyPySparkApp-0.1-py3-none- any.whl app.py'.
D) Upload 'app.py' only and submit using 'spark-submit app.py'.

Solutions: