Exam Professional Machine Learning Engineer topic 1 question 217 discussion

Actual exam question from Google's Professional Machine Learning Engineer

Question #: 217
Topic #: 1

[All Professional Machine Learning Engineer Questions]

Your team frequently creates new ML models and runs experiments. Your team pushes code to a single repository hosted on Cloud Source Repositories. You want to create a continuous integration pipeline that automatically retrains the models whenever there is any modification of the code. What should be your first step to set up the CI pipeline?

A. Configure a Cloud Build trigger with the event set as "Pull Request"
B. Configure a Cloud Build trigger with the event set as "Push to a branch"
C. Configure a Cloud Function that builds the repository each time there is a code change
D. Configure a Cloud Function that builds the repository each time a new branch is created

Show Suggested Answer

Suggested Answer: B 🗳️

by pikachu007 at Jan. 13, 2024, 6:06 a.m.

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forport

10 months, 3 weeks ago

Selected Answer: B

B. According to Gemini-Advanced.

upvoted 1 times

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fitri001

1 year, 2 months ago

Selected Answer: B

Continuous Integration: CI pipelines aim for frequent integration of code changes. Triggering the build pipeline upon every push to a branch (including the main branch) ensures your models retrain whenever the code relevant to them is modified. Focus on Relevant Changes: Compared to option A ("Pull Request"), triggering on pushes allows retraining even for direct pushes to the main branch, not just pull request merges. This can be crucial for catching critical code changes that might bypass pull requests.

upvoted 2 times

fitri001

1 year, 2 months ago

C. Cloud Function for Code Changes: While Cloud Functions can be used for CI pipelines, manually configuring a function for every code change might become cumbersome and less scalable compared to a dedicated CI/CD service like Cloud Build with built-in triggering functionalities. D. Cloud Function for New Branches: Triggering on new branch creation alone wouldn't retrain models on existing branches where your team actively works. You'd need an additional trigger for existing branches (e.g., push to branch) to achieve automatic retraining.

upvoted 1 times

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pinimichele01

1 year, 2 months ago

Selected Answer: B

For ANY modifications, “Push to a branch” is the best choice in Cloud Build trigger.

upvoted 1 times

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guilhermebutzke

1 year, 4 months ago

Selected Answer: B

My Answer B: For ANY modifications, “Push to a branch” is the best choice in Cloud Build trigger. However, when it comes to ML model training, retraining models on every push might be resource-intensive, especially if the training process is computationally expensive. So, I think triggering the CI pipeline on a pull request allows for changes to be tested before merging into the main branch. would be a better choice …

upvoted 1 times

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b1a8fae

1 year, 5 months ago

Selected Answer: B

B. Any code change on the Cloud repo is done by pushing to a branch.

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pikachu007

1 year, 5 months ago

Selected Answer: B

Cloud Build Integration: Cloud Build is Google Cloud's fully managed CI/CD platform, designed to automate builds and deployments, making it ideal for this task. Trigger on Code Pushes: Setting the trigger event to "Push to a branch" ensures that the pipeline automatically activates whenever new code is pushed to any branch of the repository, aligning with the goal of retraining models on code modifications.

upvoted 4 times

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