An AI team is using Spark Applications to analyze and format data for AI fine-tuning.

B is correct. Airflow is the workflow-orchestration component in HPE AI Essentials. It is designed to define directed acyclic graphs (DAGs) whose tasks execute in a controlled sequence based on dependencies. If a team has several Spark applications that prepare, transform, and validate data before model fine-tuning, Airflow can schedule those applications as stages in a repeatable pipeline and coordinate retries, dependencies, and execution order.

Katib and Ray Tune are focused on hyperparameter optimization rather than general-purpose workflow orchestration. MLflow tracks experiments, runs, artifacts, and model lifecycle information; it is not the scheduler that executes an ordered series of Spark jobs. Airflow complements these components rather than replacing them.

The key exam distinction is between orchestration and execution engines. Spark performs distributed data processing. Airflow controls when the Spark applications run and how one stage depends on another. That separation permits the data-preparation pipeline to be automated without embedding orchestration logic inside each Spark application.

References/topics: Advanced HPE Compute Solutions, Rev. 26.21, Module 4 “Using Spark” and “Airflow”; HPE AI Essentials Software Airflow documentation.