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Cloudera CDP-3002 Exam Syllabus Topics:

Cloudera CDP Data Engineer - Certification Sample Questions:

1. How can you secure your data pipelines within the Cloudera Data Engineering service to ensure data privacy and compliance?

A) Implement encryption for data at rest and in transit but ignore user access control.
B) Rely solely on access control lists (ACLs) defined on individual data assets.
C) Employ a layered security approach combining access control, encryption, and audit logging.
D) Utilize Cloudera Manager security features without additional configuration within the Data Engineering service.

2. A colleague proposes using Iceberg's optimistic locking feature for a high-concurrency scenario where multiple jobs frequently modify the same table. What potential issues should you raise?

A) Iceberg's optimistic locking requires manual conflict resolution.
B) Optimistic locking might result in many failed transactions and retries, potentially leading to job instability.
C) Optimistic locking in Iceberg introduces a significant performance overhead.
D) Iceberg's optimistic locking isn't suitable for distributed environments like CDP.

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

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

4. You want to use Spark to perform aggregations on data stored in Hive tables. How can you achieve this efficiently and seamlessly?

A) Leverage Spark SQL's built-in aggregation functions like SUM and COUNT
B) Use HiveQL's aggregation capabilities and then convert the results back to a Spark DataFrame
C) Write custom aggregation logic using Spark functions and loop through the entire DataFrame
D) Implement custom UDFs (User-Defined Functions) in Spark for complex aggregations

5. You encounter an error during the execution of your Airflow DAG. How can you identify the root cause of the issue and debug it effectively?

A) Utilize Airflow logs (scheduler, worker) to analyze detailed error messages and stack traces.
B) All of the above
C) Rely on the error message displayed on the Airflow UI, even if it's generic.
D) Inspect the code of failed tasks directly within the Airflow web Ul for potential issues.

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