Free Cloudera CDP-3002 Practice Test & Real Exam Questions

Exam Code/Number: CDP-3002
Exam Name/Title: CDP Data Engineer - Certification Exam
Certification Provider: Cloudera
Corresponding Certification: Cloudera Certification

Exam Questions: 320
Updated On: Jun 29, 2026

Question #1

What is the impact of setting the Spark configuration spark.sql.autoBroadcastJoinThreshold to -1?

A. It increases the threshold for choosing which table to broadcast in a join, potentially improving join performance.

B. It automatically selects the optimal threshold for broadcasting based on the cluster's current workload.

C. It sets an unlimited threshold for broadcasting tables, which may cause out-of-memory errors.

D. It disables the broadcast join feature, forcing all joins to be shuffled joins.

Discussion 0

Correct Answer: D Vote an answer

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Question #2

Your project involves integrating Spark with a NoSQL database, MongoDB. You need to write a DataFrame 'df into a MongoDB collection named 'orders'. Which PySpark code snippet correctly achieves this?

Discussion 0

Correct Answer: A Vote an answer

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Question #3

You have deployed a Spark application on Kubernetes, which is experiencing intermittent failures. To improve fault tolerance, you decide to implement checkpointing. Which of the following is the best approach to add checkpointing in a PySpark application?

A. Implement checkpointing at the application level outside of Spark.

D. Enable checkpointing in Kubernetes configuration files.

Discussion 0

Correct Answer: B Vote an answer

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Question #4

Your Spark application encounters performance issues when reading data from a large Hive table. What potential optimization techniques can you explore?

A. Leverage partition pruning to only read relevant data from the table

B. Use a different file format for the Hive table, like CSV, for faster parsing

C. Increase the number of Spark executors without further optimization

D. Implement custom data compression logic within Spark for improved storage efficiency

Discussion 0

Correct Answer: A Vote an answer

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Question #5

For improving join performance, why is it recommended to filter data before joining tables in Apache Spark?

A. To reduce the volume of data processed during the join

B. To increase the amount of data being shuffled

C. To prepare data for broadcast joins irrespective of size

D. To enforce strict data typing across joined datasets

Discussion 0

Correct Answer: A Vote an answer

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Question #6

A PySpark application is facing performance issues due to uneven distribution of data across the nodes. Which approach would best help in resolving this issue?

A. Convert the DataFrame to an RDD and then back to a DataFrame.

B. Use 'df.collect()'.

C. Employ to redistribute data based on a key.

D. Apply 'df.coalesce(Y to reduce the number of partitions.

Discussion 0

Correct Answer: C Vote an answer

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Question #7

You're deploying your Airflow DAGs to a production environment. What are some best practices to ensure reliability and maintainability?

A. Grant full access to the Airflow web UI to everyone for monitoring and troubleshooting purposes.

B. Implement version control for your DAG code and configuration files to track changes and facilitate rollbacks.

C. Schedule DAGs to run as frequently as possible to ensure real-time data processing.

D. Use the same Airflow environment for development, testing, and production deployments.

Discussion 0

Correct Answer: A,B Vote an answer

Question #8

You need to handle potential errors and retries within your Airflow ETL pipeline. How can you achieve this functionality?

A. Implement exception handling within your custom Python operators and define retry behavior manually.

B. Use the BranchPythonOperator to check for specific error conditions and branch the DAG execution accordingly.

C. All of the above

D. Configure the DAG to retry failed tasks automatically with a set number of retries.

Discussion 0

Correct Answer: C Vote an answer

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Question #9

You're tasked with deploying a new Airflow DAG to production. What are some key considerations for ensuring a smooth and successful deployment?

A. Utilize infrastructure as code (laC. tools like Terraform to manage Airflow deployment and configuration in a consistent and repeatable manner.

B. Deploy the DAG directly to the production environment without any testing or staging phase.

C. All of the above

D. Thoroughly test the DAG in a staging environment before deploying it to production.

Discussion 0

Correct Answer: A,C,D Vote an answer

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