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Cloudera CDP Data Engineer - Certification Sample Questions:

1. How can you implement a data quality check in Apache Airflow that verifies the row count of a table does not decrease from the previous DAG run?

A) Utilize the PreviousDagRunSensor with a custom Python function for comparison.
B) Use the BranchPythonOperator to branch the workflow based on the row count comparison logic.
C) Implement a custom SqlSensor that checks the row count against a stored value in XComs.
D) Store the row count from the previous run in Airflow Variables and compare it using a Pythonoperator.

2. In the context of Cloudera's Optimization Framework, what role does data statistics collection play?

A) It provides metadata for security enforcement
B) It is used to generate more data
C) It reduces the need for data compression
D) It helps the optimizer make informed decisions about data layout and query execution plans

3. In the context of caching data for reuse in Spark, how does the Tungsten project contribute to enhancing memory management and execution efficiency?

A) By optimizing Spark's internal memory management and data processing operations
B) By enforcing strict data typing to reduce runtime type checking overhead
C) By rewriting Spark operations in a lower-level language for direct memory access
D) By providing a graphical interface for memory management

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

5. If you want to set a minimum and maximum number of Executor pods for a Spark application in Kubernetes, which pair of PySpark configuration settings would you use?

A) 'spark.kubernetes.container.image', 'spark.kubernetes.executor.limit.cores'
B) 'spark.executor.memory', 'spark.executor.memoryoverhead'
C) 'spark.dynamicAllocation.minExecutors', 'spark.dynamicAllocation.maxExecutors'
D) 'spark.executor.instances', 'spark.executor.cores'

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