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Cloudera CDP Data Engineer - Certification Sample Questions:

1. Your Airflow DAG involves tasks that require access to confidential data like passwords or API keys. How can you securely manage and access these credentials within the DAG?

A) Utilize environment variables to store the credentials and access them within the tasks.
B) Implement a custom secret management solution outside of Airflow.
C) All of the above
D) Store the credentials directly within the DAG code, assuming limited access to the codebase.

2. Your Iceberg table experiences frequent schema evolution, with new columns being added regularly. Which of the following might you need to adjust to maintain optimal performance?

A) The Iceberg table's write format (vl or v2)
B) Spark shuffle partitions (spark.sql.shuffle.partitions)
C) Kubernetes resource requests for Spark executors
D) Iceberg metadata table pruning frequency

3. When performing a bucketed join between two tables, what must be true for the join to be executed as a map-side join, thereby maximizing performance?

A) Both tables must be partitioned, not bucketed, on the join columns.
B) Both tables must be bucketed on the join columns with a different number of buckets.
C) Only one table needs to be bucketed, regardless of the bucket count.
D) Both tables must be bucketed on the join columns with the same number of buckets and the same hash function.

4. You're building an Airflow DAG that involves multiple data processing tasks. How can you handle task dependencies and ensure the tasks execute in the correct order?

A) Leverage XCom to share data between tasks and define dependencies based on the data availability.
B) All of the above
C) Use the Airflow UI to visually connect the tasks with arrows, indicating their execution order.
D) Define the tasks directly within the DAG code using Python operators and set their dependencies manually.

5. You have an Airflow DAG that includes tasks for data extraction, transformation, and loading. You notice that the transformation tasks are computationally intensive and are causing delays in the DAG's execution. To optimize performance, you decide to offload these tasks to a cloud-based service that can scale dynamically. Which approach ensures minimal changes to the DAG structure while integrating this optimization?

A) Implement the transformation tasks as DockerOperator tasks, with each task running in a containerized environment on the cloud service.
B) Use the ExternalTaskSensor to wait for the transformation to complete on the cloud service before proceeding.
C) Replace the transformation tasks with HttpSensor tasks that trigger the cloud service and poll for completion.
D) Modify the transformation tasks to use the PythonOperator to make API calls to the cloud service, handling the transformation.

Solutions: