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Cloudera CDP-3002 Exam Syllabus Topics:

Cloudera CDP Data Engineer - Certification Sample Questions:

1. Your Airflow DAG involves tasks that require access to specific resources like databases or external services. How can you ensure these resources are available and properly configured for the DAG execution?

A) Utilize Airflow connections to store and manage resource details securely.
B) Implement custom logic within each task to dynamically discover and connect to resources.
C) Grant everyone access to all resources to avoid potential configuration issues.
D) Hardcode connection details (credentials, URLs) directly within the DAG code.

2. You encounter an error message stating "Failed to find persisted data for RDD" in your Spark application. What are the potential causes and how can you troubleshoot them?

A) The data might have been corrupted or deleted from the storage location
B) All of the above
C) Spark encountered a network issue and couldn't access the persisted data
D) The RDD was never persisted, or the storage level was set incorrectly

3. How can you leverage Spark Streaming for real-time data processing and analytics?

A) By utilizing Structured Streaming with Kafka as the source and sink.
B) By implementing custom logic for data ingestion, transformation, and output.
C) Both A and B.
D) By defining a streaming DataFrame with a window function.

4. Why are partitioned tables beneficial in Hive for large datasets?

A) They allow for faster query processing by scanning only relevant partitions.
B) They ensure that all data is stored in a single file, simplifying access.
C) They decrease the security of the data.
D) They automatically encrypt data based on the partition key.

5. When leveraging caching in Spark, which scenario illustrates the use of the MEMORY ONLY SER storage level most effectively?

A) Caching datasets that require fast, sequential access without the need for serialization.
B) Caching small, static datasets used in lookup operations.
C) Caching datasets that are frequently accessed and modified.
D) Caching large datasets that do not fit into memory when stored in deserialized form.

Solutions: