Cloudera CDP Data Engineer - Certification - CDP-3002

Cloudera CDP-3002 test insides dumps
  • Exam Code: CDP-3002
  • Exam Name: CDP Data Engineer - Certification Exam
  • Updated: Jul 01, 2026
  • Q & A: 320 Questions and Answers
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Cloudera CDP Data Engineer - Certification Sample Questions:

1. In the context of caching data for reuse in Spark, how does the Tungsten project contribute to enhancing memory management and execution efficiency?

A) By optimizing Spark's internal memory management and data processing operations
B) By providing a graphical interface for memory management
C) By enforcing strict data typing to reduce runtime type checking overhead
D) By rewriting Spark operations in a lower-level language for direct memory access


2. 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) Leverage Spark SQL's schema inference capabilities to automatically adjust the DataFrame schema
C) Modify the Spark DataFrame schema to match the Hive table schema manually
D) Ignore the schema mismatch and write the data anyway, potentially leading to data corruption


3. 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) Use the same Airflow environment for development, testing, and production deployments.
D) Schedule DAGs to run as frequently as possible to ensure real-time data processing.


4. In Hive, what impact does setting the hive.exec.dynamic.partition.mode to nonstrict have on dynamic partitioning operations?

A) It enforces that at least one static partition be specified in the query.
B) It disables dynamic partitioning, requiring all partitions to be predefined.
C) It restricts the insertion of data to existing partitions only.
D) It allows the creation of new partitions dynamically based on the input data values.


5. In Apache Airflow, which strategy allows for the dynamic generation of tasks within a DAG based on external data sources, such as a list of database tables?

A) Employing the TaskFlow API with dynamic task mapping
B) Utilizing the @dag decorator with dynamic input parameters
C) Using the Variable class to store and retrieve the list of tables
D) Implementing a PythonOperator that generates other tasks at runtime


Solutions:

Question # 1
Answer: A
Question # 2
Answer: B
Question # 3
Answer: A,B
Question # 4
Answer: D
Question # 5
Answer: A

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