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Cloudera CDP-3002 Exam Syllabus Topics:

Cloudera CDP Data Engineer - Certification Sample Questions:

1. What technique can be employed to optimize join performance by reducing data shuffle across the network?

A) Increasing the number of partitions
B) Decreasing the memory allocated to executors
C) Partitioning both datasets on a different key
D) Using broadcast joins for smaller datasets

2. When configuring a Spark job to run on Kubernetes within the Cloudera Data Engineering (CDE. service, which property is crucial for specifying the Docker image to be used by the executors?

A) spark.cde.container.image
B) spark.executor.image
C) spark.kubernetes.container.image
D) spark.kubernetes.executor.container.image

3. You need to design a DAG that can be easily monitored and visualized for performance insights. How can you achieve this?

A) Utilize Airflow's built-in metrics and monitoring features like the Airflow web UI to track DAG execution and task performance.
B) Implement alerts and notifications within the DAG to trigger upon specific events or performance thresholds.
C) Implement custom logic within each task to send detailed performance metrics to external monitoring tools.
D) All of the above

4. An Iceberg job fails with an "out of memory" error. Which Spark configuration changes might help? (Choose two)

A) Enable Spark broadcast joins
B) Decrease the size of Spark partitions (spark.sql.shuffle.partitions)
C) Upgrade to a larger Kubernetes pod size.
D) Increase executor memory (spark.executor.memory)
E) Increase driver memory (spark.driver.memory)

5. You want to schedule your Airflow DAG to run every hour, starting at midnight (00:00). How can you achieve this scheduling configuration?

A) Utilize Airflow triggers to initiate the DAG execution at midnight.
B) Set the schedule_interval parameter to "0 0 ".
C) Use the cron expression "@hourly".
D) Define a custom Python function to schedule the DAG at the desired time.

Solutions: