Google Professional Machine Learning Engineer exam dumps

Google Professional Machine Learning Engineer practice question 168 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 168

Select 3Google Cloud Platform

Your team is building a machine learning workflow on Google Cloud that includes training and deploying a model using Vertex AI. You want to integrate your code source repository to automate the deployment process whenever new changes are pushed to the repository's main branch. Which of the following steps would you take to implement this integration?

  1. A

    Use Cloud Build triggers to listen for changes in the code repository and execute a build pipeline.

  2. B

    Store the repository credentials in a Google Cloud Storage bucket for secure access.

  3. C

    Configure a CI/CD pipeline using Cloud Build and a source repository like Cloud Source Repositories, GitHub, or Bitbucket.

  4. D

    Use Vertex AI Pipelines to directly monitor the repository for changes and trigger deployments automatically.

  5. E

    Ensure that the IAM roles for Cloud Build have permissions to access the source repository and deploy to Vertex AI.

Show answer and explanation

Correct answers: A, C, E

Explanation

Integrating code source repositories into a CI/CD pipeline for machine learning workflows requires using Cloud Build triggers to automate builds, configuring a pipeline with a supported code repository, and ensuring the correct IAM permissions are in place. These steps ensure a secure and efficient process for deploying changes to Vertex AI. Storing credentials in Cloud Storage or relying solely on Vertex AI Pipelines for repository monitoring are not recommended or supported practices.

  • A. Correct.

    Cloud Build triggers allow you to configure automated build pipelines that respond to changes in the code repository, which is essential for integrating CI/CD workflows.

  • B. Incorrect.

    Storing repository credentials in a Google Cloud Storage bucket is not a secure or recommended practice. Instead, use built-in integrations or secret management tools.

  • C. Correct.

    A CI/CD pipeline configured with Cloud Build and a supported source repository enables automated workflows for building, testing, and deploying the machine learning application.

  • D. Incorrect.

    Vertex AI Pipelines cannot directly monitor a code repository for changes. Instead, they are used for orchestrating ML workflows and can be triggered within a CI/CD pipeline.

  • E. Correct.

    Granting appropriate IAM roles to Cloud Build ensures it can access the source repository and deploy the model to Vertex AI, which is critical for the integration.

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