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Cloudera CDP-3002 Exam Syllabus Topics:

Cloudera CDP Data Engineer - Certification Sample Questions:

Question 1

When writing a DataFrame to a CSV file, what potential issues should you consider and how can you address them?

A. All of the above
B. Choose an appropriate compression format like Gzip to reduce file size
C. Ensure proper handling of special characters and delimiters to avoid data corruption
D. No specific issues need to be considered, as CSV is a simple format

Question 2

Your Airflow DAG involves sending notifications upon successful completion of the entire pipeline. How can you achieve this functionality?

A. Configure the Airflow web UI to send alerts based on DAG run status.
B. Use the Email Operator to send an email notification upon successful DAG run completion.
C. Implement a custom notification script within the final task of the DAG.
D. Utilize Airflow variables to store notification details and access them within the final task.

Question 3

You have a DataFrame containing sales data with columns "product_id", "customer id", and "amount". How can you efficiently calculate the total sales per customer?

A. Implement a custom function to group and sum the sales
B. Utilize spark SQL's GROUP BY and SUM functions
C. Leverage Spark's machine learning library (MLIiB. for aggregation
D. Use a loop to iterate through the DataFrame and accumulate the sales for each customer

Question 4

You want to schedule your ETL pipeline to run daily at 5:00 AM. How can you configure the DAG's scheduling?

A. Set the schedule_interval parameter to "0 5
B. Use the cron expression "daily at 5:00".
C. Utilize Airflow triggers to initiate the DAG execution at 5:00 AM daily.
D. Define a custom Python function to schedule the DAG at the desired time.

Question 5

How can you leverage the Cloudera Data Engineering service to monitor the performance and health of your data pipelines?

A. Manually review log files generated by individual pipeline steps.
B. Utilize the built-in monitoring capabilities of the Cloudera Data Engineering service, including dashboards and alerts.
C. Utilize custom scripting to collect and analyze pipeline metrics.
D. Integrate the service with a separate external monitoring tool.

Solutions: