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Cloudera CDP-3002 Exam Syllabus Topics:

Cloudera CDP Data Engineer - Certification Sample Questions:

1. You need to design your Airflow DAG for data quality checks to be easily monitored and visualized. How can you achieve this?

A) Implement custom logic to send detailed logs about the check results to external monitoring tools.
B) Leverage Airflow's built-in metrics and monitoring features like the Airflow web UI to track check execution and results.
C) All of the above
D) Utilize Airflow XCom to share the outcome of the checks (success/failurE. with downstream tasks for further processing.

2. What is a primary benefit of schema-on-read compared to schema-on-write in big data processing frameworks?

A) It mandates manual intervention for each data read operation.
B) It allows for greater flexibility in data formats and structures at the time of analysis.
C) It requires all data to conform to a rigid schema before storage.
D) It significantly reduces the computational overhead during data ingestion.

3. You're using Iceberg for streaming data ingestion. A critical use case requires exactly-once processing guarantees. Which considerations are most important for achieving this? (Choose two)

A) Carefully configuring the streaming source to handle retries and failures
B) Leveraging a CDC (Change Data Captur tool integrated with Iceberg.
C) Disabling Iceberg's snapshot isolation feature.
D) Using a data format like JSON, which is less prone to corruption than Parquet.
E) Enabling Iceberg's transactional writes

4. 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) Spark encountered a network issue and couldn't access the persisted data
C) The RDD was never persisted, or the storage level was set incorrectly
D) All of the above

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

Solutions: