Google Professional Machine Learning Engineer exam dumps

Google Professional Machine Learning Engineer practice question 402 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 402

Single answerGoogle Cloud Platform

Your company has a machine learning model deployed on Google Cloud’s AI Platform and processes sensitive data. The company needs to comply with strict data residency regulations, requiring that some data remains on-premises while other datasets must be processed in another cloud provider. Which strategy should you use to ensure compliance and seamless integration of your machine learning workflows?

  1. A

    Use a hybrid cloud strategy with Google Anthos to manage workloads across on-premises and other cloud environments.

  2. B

    Migrate all workloads to Google Cloud to ensure a unified environment for machine learning.

  3. C

    Deploy the model only on-premises to avoid data residency issues altogether.

  4. D

    Use a multicloud strategy with Cloud Run to split workloads between Google Cloud and other cloud providers.

Show answer and explanation

Correct answer: A

Explanation

A hybrid cloud strategy using Google Anthos is the best approach for managing machine learning workloads in a scenario where data residency regulations require part of the data to remain on-premises while leveraging cloud environments for other parts of the workflow. Anthos enables seamless integration, governance, and scalability across environments, ensuring compliance and operational efficiency.

  • A. Correct.

    This is correct. A hybrid cloud strategy with Google Anthos allows you to manage and deploy workloads across on-premises, Google Cloud, and other cloud environments while ensuring data residency requirements are met.

  • B. Incorrect.

    This is incorrect. Migrating all workloads to Google Cloud might not comply with data residency regulations that require some data to stay on-premises.

  • C. Incorrect.

    This is incorrect. Deploying the model only on-premises limits scalability and may not leverage the full capabilities of cloud-based machine learning services.

  • D. Incorrect.

    This is incorrect. While Cloud Run offers flexibility for serverless workloads, it does not directly address hybrid or multicloud strategies or ensure compliance with data residency requirements.

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