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Cloudera CDP Data Engineer - Certification Sample Questions:

1. What is a primary benefit of schema-on-read compared to schema-on-write in big data processing frameworks?

A) It requires all data to conform to a rigid schema before storage.
B) It mandates manual intervention for each data read operation.
C) It allows for greater flexibility in data formats and structures at the time of analysis.
D) It significantly reduces the computational overhead during data ingestion.

2. A data engineer is deploying a Spark application on a Kubernetes cluster. To minimize downtime during updates, which Kubernetes deployment strategy should be considered?

A) Node Affinity
B) Persistent Volumes
C) Rolling Updates
D) Pod Autoscaling

3. What impact does the Spark configuration parameter spark.network.timeout have on Spark streaming applications?

A) It determines the timeout for connections between executors and the Spark driver, impacting task retries and executor loss detection.
B) It specifies the timeout for network operations such as data shuffle and broadcast.
C) It controls the maximum time a Spark streaming application will wait for data before timing out.
D) It sets the timeout for Akka-based network operations, crucial for Spark streaming's stability.

4. You're integrating data quality checks into a complex ETL pipeline with numerous tasks and dependencies. How can you ensure the checks are executed in the correct order and don't interfere with other pipeline tasks?

A) Utilize Airflow upstream/downstream dependencies to define the execution order between check tasks and other pipeline tasks.
B) Implement a custom script to manage the execution of the data quality checks independently.
C) Run all tasks (ETL and checks) concurrently, assuming they are independent.
D) Schedule the data quality checks as a separate DAG and trigger it after the ETL pipeline completes.

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

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