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Cloudera CDP-3002 Exam Syllabus Topics:

Cloudera CDP Data Engineer - Certification Sample Questions:

1. You're deploying your Airflow DAGs to a production environment. What are some key considerations for ensuring security and reliability?

A) Configure Airflow to run with high resource limits to handle unexpected spikes in workload.
B) Implement role-based access control (RBAC. to restrict access to sensitive DAGs and resources.
C) Schedule DAG runs as frequently as possible to ensure real-time data processing.
D) Disable task logging to improve DAG execution performance.

2. Which operator or feature in Apache Airflow can be used to dynamically adjust the schedule of data quality checks based on the volume of incoming data?

A) The ExternalTaskSensor to trigger data quality checks based on external events
B) A custom PythonOperator that modifies the DAG's schedule interval
C) The BranchPythonOperator to choose between different scheduling paths
D) The Scheduler component with dynamic DAG generation

3. You are working with a large, skewed dataset in Spark. How would you optimize processing to mitigate the impact of skew and improve performance?

A) Addressing skewed data requires
B) Broadcast the skewed data to all executors.
C) Implement custom partitioners to evenly distribute skewed values.
D) Use salting on the skewed column during data partitioning.

4. What mechanism does Airflow provide to retry failed tasks?

A) Airflow Scheduler's automatic rerun feature
B) The retry_delay and retries parameters in task definitions
C) Manual intervention and rerun via the Airflow Webserver
D) The on failure callback function in DAG definitions

5. What is the recommended way to handle dependencies between data quality checks in Apache Airflow to ensure that checks are performed in a specific sequence?

A) Explicitly set task dependencies using the set_upstream or set_downstream methods.
B) Use the depends_on_past parameter in each data quality check task.
C) Use the SequentialExecutor for the Airflow environment.
D) Implement each data quality check as a separate DAG.

Solutions: