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Cloudera CDP Data Engineer - Certification Sample Questions:

1. You're working with a complex data pipeline involving both Spark and Hive operations. How can you ensure data consistency and avoid data corruption across different stages?

A) Manually manage data consistency through custom code
B) Leverage ACID transactions in both Spark and Hive
C) Rely solely on Spark's checkpointing capabilities
D) Use separate clusters for Spark and Hive processing

2. How can you ensure that a set of tasks in an Airflow DAG are executed in parallel after a specific initial task is completed?

A) Use the SequentialExecutor
B) Use the parallelism parameter in the airflow.cfg file
C) Use the ]] and [[ operators to set task dependencies
D) Set depends_on_past=True for all tasks

3. In a Kubernetes environment, why is it beneficial to run the Spark Driver in its own pod?

A) To reduce the cost of cloud resources.
B) To automatically scale the Driver based on workload.
C) To isolate the Driver from Executor pods for security reasons.
D) To use a different programming language for the Driver.

4. 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) Implement a single custom Python script containing the entire ETL logic and call it within a BashOperator.
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) Leverage dedicated operators like PostgresHook for extraction, custom Python operators for transformation, and S3Hook for loading.

5. You're working with a complex data pipeline involving Spark and Hive, and you need to monitor its performance and identify potential bottlenecks. Which tools and techniques can you employ for effective monitoring?

A) Manually analyze Spark and Hive logs after job completion
B) Leverage Spark's web UI and Hive logs for basic information
C) Utilize YARN resource manager and Spark/Hive metrics for detailed monitoring
D) Implement custom instrumentation code within your Spark application

Solutions: