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Cloudera CDP Data Engineer - Certification Sample Questions:

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

2. Which Kubernetes tool would you use to access logs from a Spark Driver running in a pod?

A) 'kubectl logs'
B) 'kubectl exec'
C) Kubernetes Dashboard
D) 'spark-submit'

3. You notice a significant performance overhead when persisting a large RDD to disk. What potential factors could contribute to this, and how can you mitigate them?

A) Using HDFS as the storage location instead of local disks
B) All of the above
C) Inefficient serialization format for the RDD data
D) Insufficient disk space on the cluster nodes

4. 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 disables backfilling entirely, ensuring that only future DAG runs are executed.
C) It forces Airflow to ignore the start_date and schedule the DAG run immediately.
D) It enables backfilling, causing Airflow to schedule DAG runs for all intervals between the DAG's start_date and the current date.

5. How can you ensure that a set of tasks in an Airflow DAG are executed in parallel after a specific initial task is completed?

A) Use the SequentialExecutor
B) Use the ]] and [[ operators to set task dependencies
C) Set depends_on_past=True for all tasks
D) Use the parallelism parameter in the airflow.cfg file

Solutions: