Google Professional Machine Learning Engineer exam dumps

Google Professional Machine Learning Engineer practice question 121 of 522

Professional Machine Learning Engineer. Professional level, Google Cloud. Free question with the correct answer and a full explanation.

Google Professional Machine Learning Engineer Question 121

Select 3Google Cloud Platform

You are building a recommendation system and need to create and manage features for your model in Vertex AI Feature Store. You have user, item, and interaction datasets that need to be used for feature engineering. Which steps should you take to ensure the features are properly created and consolidated in Vertex AI Feature Store?

  1. A

    Create separate feature groups for user, item, and interaction datasets, and define unique entity IDs for each group.

  2. B

    Use the Vertex AI Feature Store online serving capability to join features across user and item datasets at training time.

  3. C

    Store the features in a centralized feature store for reusability and consistent serving in both online and offline environments.

  4. D

    Use Vertex AI Feature Store's feature monitoring tools to automatically pre-process the raw data before storing it as features.

  5. E

    Ensure that the entity IDs are consistent across feature groups to enable accurate joins during feature retrieval.

Show answer and explanation

Correct answers: A, C, E

Explanation

To effectively use Vertex AI Feature Store for feature management, you need to create organized feature groups with unique entity IDs, centralize the features for consistency and reusability, and ensure that entity IDs are consistent across feature groups to enable accurate joins. This approach ensures proper feature consolidation and retrieval for both training and serving.

  • A. Correct.

    Correct. Creating separate feature groups for user, item, and interaction datasets with unique entity IDs ensures organized storage and retrieval of features.

  • B. Incorrect.

    Incorrect. The online serving capability of Vertex AI Feature Store is designed for fast feature retrieval, not for joining features across datasets during training time.

  • C. Correct.

    Correct. Centralizing features in Vertex AI Feature Store ensures reusability and consistency across both online and offline environments.

  • D. Incorrect.

    Incorrect. Vertex AI Feature Store does not automatically pre-process raw data. Feature engineering and pre-processing need to be done before storing the features.

  • E. Correct.

    Correct. Consistent entity IDs across feature groups are essential for accurately joining features during retrieval.

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