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Cloudera CDP-3002 Exam Syllabus Topics:

Cloudera CDP Data Engineer - Certification Sample Questions:

1. You encounter an error message stating "Task timed out" while running your Airflow DAG. What are some potential causes and how can you troubleshoot them?

A) All of the above
B) The DAG is running too frequently, overloading the system resources.
C) The extraction task is encountering issues connecting to the source system.
D) The data processing task is taking longer than the configured timeout for the task.

2. You're working with a complex data pipeline involving both Spark and Hive operations. How can you ensure data consistency and avoid data corruption across different stages?

A) Leverage ACID transactions in both Spark and Hive
B) Manually manage data consistency through custom code
C) Rely solely on Spark's checkpointing capabilities
D) Use separate clusters for Spark and Hive processing

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

A) To enforce strict data typing across joined datasets
B) To increase the amount of data being shuffled
C) To prepare data for broadcast joins irrespective of size
D) To reduce the volume of data processed during the join

4. You are designing a data pipeline that involves ingesting data from multiple sources, performing data transformations using Spark, and storing the results in a data lake. How would you leverage the Cloudera Data Engineering service to ensure efficient and fault-tolerant execution?

A) Utilize separate Spark jobs for each data source and transformation step.
B) Develop a single Spark job containing all transformation logic.
C) Design the pipeline with stages and steps, leveraging Spark operators for transformations and utilizing retries and error handling mechanisms.
D) Implement custom logic within the YAML configuration file to manage data flow and error handling.

5. For automating the deployment of Spark applications within a Cloudera Data Engineering (CDE. environment using the CDE CLI, what is the primary consideration to ensure seamless integration with existing CI/CD pipelines?

A) Embedding CDE CLI commands within pipeline scripts and managing credentials securely
B) Converting all Spark code to be Kubernetes-native before deployment
C) Ensuring all Spark applications are containerized before deployment
D) Utilizing the cde job create command with appropriate flags for version control integration

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