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Cloudera CDP-3002 Exam Syllabus Topics:

Cloudera CDP Data Engineer - Certification Sample Questions:

1. You're tasked with scheduling an ETL pipeline in Airflow that extracts data from a database, transforms it, and loads it into a data warehouse. How can you achieve this using Airflow operators?

A) Leverage dedicated operators like PostgresHook for extraction, custom Python operators for transformation, and S3Hook for loading.
B) Utilize Airflow's built-in ETL operators like BigQueryOperator or MySqlOperator (these operators are specific to certain data sources and not generally applicable for all ETL pipelines).
C) Use three separate Python operators for each stage (extract, transform, loaD. and chain them together in the DAG.
D) Implement a single custom Python script containing the entire ETL logic and call it within a BashOperator.

2. You are working with a complex Spark application involving multiple stages, and you want to ensure that later stages only start processing after all data from the previous stage is complete. How can you achieve this dependency management in Spark?

A) Rely on Spark's lineage tracking and stage boundaries to enforce dependencies
B) Use explicit persist() calls with different storage levels for each stage
C) Implement custom logic with synchronization mechanisms between stages
D) Leverage Spark Streaming concepts like micro-batching for real-time data processing

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

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

4. Due to regulatory requirements, you need to permanently delete specific sensitive records from an Iceberg table. Which of the following techniques would be most appropriate?

A) Issue a standard Iceberg DELETE query, as deleted data will automatically be expunged from the table.
B) Implement a custom process, leveraging Iceberg's row-level updates to delete the sensitive records and then using file-level operations to physically remove the data.
C) Use Iceberg's EXPIRE SNAPSHOTS procedure to remove snapshots containing the sensitive data.
D) Modify the data files directly using low-level tools to overwrite the sensitive data.

5. You're building a Spark application that involves complex iterative data processing. Which option allows you to efficiently access and update intermediate results between iterations?

A) Store intermediate results in temporary tables using Spark SQL
B) Implement custom data structures for managing intermediate data
C) Use Spark's broadcast variables for frequently accessed data across iterations
D) Leverage Spark's in-memory caching capabilities with rdd.cache()

Solutions: