While option A might seem straightforward, it can become difficult to manage and maintain for complex workflows. Option B emphasizes effective DAG design principles:Modularization: Breaking down the workflow into smaller, reusable tasks improves clarity, maintainability, and testing. Dependencies: Defining clear dependencies between tasks ensures they execute in the correct order.

Cloudera Scheduler is a core component of the Cloudera Data Engineering service responsible for scheduling and managing data pipelines. It provides functionalities like orchestration, dependency management, and monitoring of data pipelines.

If a pod is experiencing performance issues due to CPU limitations, increasing the CPU limits in the pod's YAML configuration is the most direct way to address the issue.

The File sensor operator specifically addresses the need to wait for a file before proceeding in an Airflow DAG. Option A ensures that the downstream tasks only start after the desired file is present.

To enable dynamic allocation of executors in Spark, the configuration 'spark.dynamicAllocation.enabled' must be set to 'true'. This allows Spark to dynamically adjust the number of executors based on the workload.