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Cloudera CDP-3002 Exam Syllabus Topics:

Cloudera CDP Data Engineer - Certification Sample Questions:

Question 1

You need to monitor the performance and resource usage of your Airflow ETL pipelines. How can you achieve this?

A. Monitor the resource utilization of the entire system, assuming it reflects the pipeline's performance.
B. Implement custom monitoring scripts to track specific metrics not readily available in Airflow.
C. You cannot directly monitor Airflow pipelines; rely on external monitoring tools for the underlying infrastructure.
D. Utilize Airflow's built-in metrics and monitoring features like the Airflow web UI and command-line tools.

Question 2

You're writing a Spark application that processes streaming data in real-time. How can you create DataFrames from streaming data sources?

A. Leverage Spark Streaming's functions like createDataFrame with appropriate parameters
B. All of the above
C. Use the same methods as for batch processing, reading data from a file
D. Utilize Spark SQL's streaming capabilities with the FROM clause

Question 3

What is the difference between RDDs and DataFrames in Spark?

A. RDDs are lower-level abstractions, while DataFrames offer higher-level functionalities
B. RDDs represent distributed datasets, while DataFrames work with structured data
C. RDDs are mutable, while DataFrames are immutable
D. Both B and C

Question 4

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

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

Question 5

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

Solutions: