Cloudera CDP-3002 Actual Free Exam Questions & Community Discussion

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 03, 2026

Question #49

What mechanism does Airflow provide to retry failed tasks?

A. The retry_delay and retries parameters in task definitions

B. Manual intervention and rerun via the Airflow Webserver

C. Airflow Scheduler's automatic rerun feature

D. The on failure callback function in DAG definitions

Discussion 0

Correct Answer: A Vote an answer

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Question #50

You're working with a team of data engineers who collaborate on developing and maintaining Airflow DAGs. How can you ensure version control and maintain a consistent development workflow?

A. Grant full access to the Airflow web Ul to all team members for code modifications, regardless of their roles.

B. Implement a custom workflow management system outside of Airflow for managing DAG development.

C. Store all DAG code in a shared folder on a local file system, relying on manual coordination between team members.

D. Utilize a version control system like Git to track changes in the DAG code and enable collaborative development.

Discussion 0

Correct Answer: D Vote an answer

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Question #51

You want to perform an Iceberg table join in CDP using Spark SQL, but you notice it's much slower than expected. What could be some of the reasons? (Choose two)

A. You're joining on a column with low cardinality (few distinct values).

B. Iceberg version mismatch between Spark and CDP.

C. Spark's dynamic query execution is enabled.

D. One of the tables isn't partitioned effectively.

E. Spark is using nested loop joins instead of broadcast hash joins due to table sizes.

Discussion 0

Correct Answer: D,E Vote an answer

Question #52

What challenge does schema inference aim to address when dealing with big data ecosystems?

A. The need for large storage spaces to hold data schemas

B. Reducing the computational power required for data analysis

C. Ensuring that all data is encrypted according to its inferred schema

D. The variety and complexity of data formats and structures

Discussion 0

Correct Answer: D Vote an answer

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Question #53

Your ETL pipeline involves complex data transformations that require libraries not readily available in the Airflow environment. How can you ensure these libraries are accessible during pipeline execution?

A. Install the required libraries directly into the Airflow environment.

B. Utilize system-wide library installations, assuming they are accessible to the Airflow user.

C. Configure Airflow to use a virtual environment with pre-installed libraries.

D. Package the libraries with your DAG code and reference them within the Python operators.

Discussion 0

Correct Answer: D Vote an answer

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Question #54

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

A. It disables dynamic partitioning, requiring all partitions to be predefined.

B. It restricts the insertion of data to existing partitions only.

C. It enforces that at least one static partition be specified in the query.

D. It allows the creation of new partitions dynamically based on the input data values.

Discussion 0

Correct Answer: D Vote an answer

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Question #55

Which technologies are typically involved in schema inference processes in Cloudera Data Platform (CDP)?

A. Apache Avro

B. Apache NiFi

C. Traditional RDBMS

D. Manual scripting

E. Apache Spark

Discussion 0

Correct Answer: A,B,E Vote an answer

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Question #56

You're building an Airflow DAG to automate data quality checks on the output of your ETL pipeline. The checks involve performing various data validation tasks like checking for missing values, ensuring data type consistency, and verifying data integrity based on specific business rules. How can you implement these checks within Airflow?

A. All of the above

B. Leverage dedicated Airflow operators like BigQueryCheckOperator or S3KeySensor (these operators are specific to certain data sources and not generally applicable for all data quality checks).

C. Use the PythonOperator to write custom Python scripts for each individual check and chain them together in the DAG.

D. Utilize Python libraries like Pandas or Spark for data manipulation and validation within the PythonOperator.

Discussion 0

Correct Answer: A Vote an answer

Question #57

In the context of schema inference, which component of the Apache Spark ecosystem plays a crucial role in enabling the exploration of semi-structured data?

A. Spark Streaming

B. DataFrame API

C. Spark SQL

D. RDD (Resilient Distributed Dataset)

Discussion 0

Correct Answer: B Vote an answer

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Question #58

In Apache Airflow, how can you dynamically generate tasks for each table in your database that needs a quality check?

A. Implement a BranchPythonOperator to create branches for each table dynamically.

B. Use the SubDagOperator to create a sub-DAG for each table.

C. Use the Variable feature to store a list of tables and iterate over them with a PythonOperator.

D. Utilize the Dynamic Task Mapping feature to create a task for each table.

Discussion 0

Correct Answer: D Vote an answer

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Question #59

You're designing a scalable and fault-tolerant Spark application for processing large-scale geospatial dat a. What additional considerations should you take into account beyond the general distributed processing principles?

A. Choose a larger virtual machine size for Spark executors

B. Leverage geospatial libraries like GeoSpark for efficient spatial operations

C. Implement custom partitioning strategies based on geospatial coordinates

D. Use a dedicated cluster specifically for geospatial processing tasks

Discussion 0

Correct Answer: B,C Vote an answer

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Question #60

You need to filter a Spark DataFrame based on multiple conditions. How can you achieve this efficiently and concisely?

A. Use Spark SQL's WHERE clause with a complex expression

B. Leverage chained filter() calls with logical operators like AND and OR

C. Use multiple filter() calls with individual conditions

D. Implement custom filtering logic using loops and conditional statements

Discussion 0

Correct Answer: B Vote an answer

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