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Cloudera CDP Data Engineer - Certification Sample Questions:

1. You're tasked with deploying a new Airflow DAG to production. What are some key considerations for ensuring a smooth and successful deployment?

A) All of the above
B) Thoroughly test the DAG in a staging environment before deploying it to production.
C) Deploy the DAG directly to the production environment without any testing or staging phase.
D) Utilize infrastructure as code (laC. tools like Terraform to manage Airflow deployment and configuration in a consistent and repeatable manner.

2. You need to design your Airflow DAG for data quality checks to be scalable and manageable as the number of datasets and checks grows. How can you achieve this?

A) Leverage external configuration files (e.g., YAML or JSON) to define data quality checks and associated parameters.
B) Implement a modular design using sub-DAGs, where each sub-DAG encapsulates the data quality checks for a specific dataset.
C) Utilize Airflow variables to store configuration details like data source paths and check thresholds.
D) Hardcode all data quality checks and data sources directly within the DAG code.

3. Which feature in Apache Airflow allows you to retry a data quality check task if it fails initially due to transient issues?

A) retries parameter in the task definition
B) SLAs
C) catchup
D) Branching

4. What are the potential consequences of persisting all intermediate RDDs in Spark with the MEMORY ONLY storage level?

A) Increased risk of application crashes due to memory exhaustion
B) All intermediate data will be automatically persisted to disk
C) Improved performance due to faster data access
D) No impact on the application, as Spark manages memory efficiently

5. Your team is using PySpark and wants to ensure task re-execution in case of a node failure. What mechanism in Spark ensures that tasks are retried on other nodes upon failure?

A) Data Replication
B) Master Node Redundancy
C) Checkpointing
D) Task Re-execution

Solutions: