Databricks Certified Data Engineer - Professional
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In the context of designing a machine learning pipeline for a Databricks project, consider the following scenario: Your organization requires a scalable, maintainable solution that adheres to best practices for data preprocessing, feature engineering, model training, and model deployment. The solution must also support experiment tracking, model management, and easy integration with other Databricks applications. Given these requirements, which of the following approaches would BEST meet the organization's needs? (Choose one option.)
In the context of designing a machine learning pipeline for a Databricks project, consider the following scenario: Your organization requires a scalable, maintainable solution that adheres to best practices for data preprocessing, feature engineering, model training, and model deployment. The solution must also support experiment tracking, model management, and easy integration with other Databricks applications. Given these requirements, which of the following approaches would BEST meet the organization's needs? (Choose one option.)
Explanation:
Option B is the correct approach because it leverages Databricks notebooks for a structured, multi-step pipeline and utilizes MLflow for comprehensive experiment tracking and model management. Deploying the model as a Databricks model service ensures seamless integration with other applications within the Databricks ecosystem, aligning with the organization's requirements for scalability, maintainability, and best practices adherence.