Exam Professional Machine Learning Engineer topic 1 question 259 discussion

Actual exam question from Google's Professional Machine Learning Engineer

Question #: 259
Topic #: 1

[All Professional Machine Learning Engineer Questions]

You have developed an application that uses a chain of multiple scikit-learn models to predict the optimal price for your company’s products. The workflow logic is shown in the diagram. Members of your team use the individual models in other solution workflows. You want to deploy this workflow while ensuring version control for each individual model and the overall workflow. Your application needs to be able to scale down to zero. You want to minimize the compute resource utilization and the manual effort required to manage this solution. What should you do?

A. Expose each individual model as an endpoint in Vertex AI Endpoints. Create a custom container endpoint to orchestrate the workflow.
B. Create a custom container endpoint for the workflow that loads each model’s individual files Track the versions of each individual model in BigQuery.
C. Expose each individual model as an endpoint in Vertex AI Endpoints. Use Cloud Run to orchestrate the workflow.
D. Load each model’s individual files into Cloud Run. Use Cloud Run to orchestrate the workflow. Track the versions of each individual model in BigQuery.

Show Suggested Answer

Suggested Answer: C 🗳️

by pikachu007 at Jan. 13, 2024, 3:36 p.m.

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AzureDP900

9 months ago

Option C is right because: 1)Exposing individual models as Vertex AI Endpoints (Option C) allows for version tracking, which is essential for maintaining consistency across different workflows. 2)Using Cloud Run to orchestrate the workflow (Option C) enables you to scale down to zero and minimize compute resource utilization. 3)You want to deploy your application while ensuring version control for each individual model and the overall workflow.

upvoted 3 times

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gscharly

11 months, 2 weeks ago

Selected Answer: C

B,D not correct since BQ is not the best approach. A would require more manual work

upvoted 1 times

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guilhermebutzke

1 year, 1 month ago

My Answer: C B and D: Not Correct: Big query is not the best approach to trach versions of model. A and C: Looking for “ensuring version control for each individual mode” (endpoints), and “be able to scale down to zero”, “minimize the compute resource utilization and the manual effort required to manage this solution”, I think to use Cloud Run could be the best option for those cases. https://www.youtube.com/watch?v=nhwYc4StHIc&ab_channel=GoogleCloudTech

upvoted 4 times

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pikachu007

1 year, 2 months ago

Selected Answer: C

Option A: A custom container endpoint for orchestration adds complexity and management overhead. Option B: Loading model files directly into a custom container endpoint can lead to versioning challenges and potential conflicts if models are shared across workflows. Option D: Using BigQuery for model versioning is not its primary function and might introduce complexities in model loading and management.