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Cloudera CDP-3002 Exam Syllabus Topics:

Cloudera CDP Data Engineer - Certification Sample Questions:

1. In a Kubernetes environment, how does Kubernetes ensure fault tolerance when a pod running a Spark task fails?

A) By checkpointing RDDs
B) Using a load balancer for master components
C) By using a Standby Master setup
D) Through Pod Replication with ReplicaSets

2. You're working with a complex data pipeline involving Spark and Hive, and you need to monitor its performance and identify potential bottlenecks. Which tools and techniques can you employ for effective monitoring?

A) Manually analyze Spark and Hive logs after job completion
B) Leverage Spark's web UI and Hive logs for basic information
C) Implement custom instrumentation code within your Spark application
D) Utilize YARN resource manager and Spark/Hive metrics for detailed monitoring

3. You're facing a schema mismatch between a Spark DataFrame and a Hive table when trying to write the DataFrame to the table. What are the potential causes and how can you address them?

A) Use HiveQL's ALTER TABLE statement to modify the Hive table schema
B) Modify the Spark DataFrame schema to match the Hive table schema manually
C) Ignore the schema mismatch and write the data anyway, potentially leading to data corruption
D) Leverage Spark SQL's schema inference capabilities to automatically adjust the DataFrame schema

4. Your Spark application encounters frequent failures during the execution of a specific stage. How can you utilize Spark's persistence mechanisms to improve the application's fault tolerance?

A) Implement custom retry logic within the failing stage
B) Persist the RDDs used in the failing stage with MEMORY_ONLY storage level
C) Leverage Spark's checkpointing functionality to save application state periodically
D) Increase the number of Spark executors to handle potential resource limitations

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

Solutions: