Databricks Machine Learning Associate exam dumps

Databricks Machine Learning Associate practice question 426 of 656

Databricks Certified Machine Learning Associate. Associate level, Databricks. Free question with the correct answer and a full explanation.

Databricks Machine Learning Associate Question 426

Select 3

A data science team at a large organization is using Databricks Unity Catalog to manage their machine learning workflows. They are debating whether to create feature store tables at the account level instead of the workspace level. What are the benefits of creating feature store tables at the account level in Unity Catalog?

  1. A

    Feature store tables can be shared across multiple workspaces, enabling cross-team collaboration.

  2. B

    It improves security and compliance by centralizing access control policies for feature store tables.

  3. C

    Creating feature store tables at the account level automatically improves model training performance.

  4. D

    It reduces duplication of feature definitions across workspaces, ensuring consistency.

  5. E

    Feature store tables at the account level automatically enable real-time feature updates without additional setup.

Show answer and explanation

Correct answers: A, B, D

Explanation

Creating feature store tables at the account level in Unity Catalog provides benefits such as cross-workspace sharing, centralized access control for improved security, and reduced duplication of feature definitions. These advantages align with the principles of collaboration, governance, and consistency in machine learning workflows. However, performance and real-time updates are not inherently tied to the account-level storage of feature store tables.

  • A. Correct.

    Correct: Creating feature store tables at the account level allows them to be shared across multiple workspaces, which is beneficial for cross-team collaboration and reusability.

  • B. Correct.

    Correct: Unity Catalog centralizes access control policies at the account level, which enhances security and compliance for feature store tables.

  • C. Incorrect.

    Incorrect: Model training performance is not directly impacted by the storage location of feature store tables. Performance depends more on the underlying compute resources and data locality.

  • D. Correct.

    Correct: Storing feature definitions at the account level reduces redundancy and ensures consistency across workspaces, as all teams reference the same feature definitions.

  • E. Incorrect.

    Incorrect: Real-time updates to feature store tables depend on specific configurations and pipelines; creating them at the account level does not automatically enable this capability.

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