Google Professional Machine Learning Engineer exam dumps

Google Professional Machine Learning Engineer practice question 327 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 327

Select 3Google Cloud Platform

Your team is building an ML pipeline that predicts customer churn. You want to use Vertex AI Feature Store to manage features for this pipeline. The team has identified several requirements:

  1. Features should be reusable across multiple ML models.
  2. Features need to be updated in real-time as new customer data arrives.
  3. Historical feature values should be stored to enable time-travel queries for model training. Which of the following capabilities of Vertex AI Feature Store can help meet these requirements?
  1. A

    Feature sharing across multiple ML models and pipelines using centralized feature repositories

  2. B

    Real-time feature updates using online serving capabilities

  3. C

    Support for time-travel queries through feature value versioning

  4. D

    Automatic hyperparameter tuning for features stored in the Feature Store

  5. E

    Built-in explainability for features stored in the Feature Store

Show answer and explanation

Correct answers: A, B, C

Explanation

Vertex AI Feature Store is designed to manage ML features efficiently, providing reusability, real-time updates, and historical feature storage. These capabilities directly address the team's requirements of feature sharing, real-time updates, and time-travel queries. However, tasks like hyperparameter tuning and explainability are outside the scope of the Feature Store and are handled by other Vertex AI components.

  • A. Correct.

    Correct: Vertex AI Feature Store provides a centralized feature repository which makes it easy to reuse features across multiple ML models and pipelines.

  • B. Correct.

    Correct: Vertex AI Feature Store supports online serving, enabling real-time updates of feature values as new data becomes available.

  • C. Correct.

    Correct: Vertex AI Feature Store supports feature value versioning, which allows time-travel queries for retrieving historical feature values.

  • D. Incorrect.

    Incorrect: Hyperparameter tuning is not a capability of Vertex AI Feature Store. It is a feature of Vertex AI Training and other related tools.

  • E. Incorrect.

    Incorrect: While Vertex AI provides tools for explainability, this is not specific to the Feature Store. Feature Store is focused on feature management and serving.

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