Exam Professional Machine Learning Engineer topic 1 question 301 discussion

Actual exam question from Google's Professional Machine Learning Engineer

Question #: 301
Topic #: 1

[All Professional Machine Learning Engineer Questions]

You are an ML engineer at a bank. You need to build a solution that provides transparent and understandable explanations for AI-driven decisions for loan approvals, credit limits, and interest rates. You want to build this system to require minimal operational overhead. What should you do?

A. Deploy the Learning Interpretability Tool (LIT) on App Engine to provide explainability and visualization of the output.
B. Use Vertex Explainable AI to generate feature attributions, and use feature-based explanations for your models.
C. Use AutoML Tables with built-in explainability features, and use Shapley values for explainability.
D. Deploy pre-trained models from TensorFlow Hub to provide explainability using visualization tools.

Show Suggested Answer

Suggested Answer: C 🗳️

by CassiniExam at Feb. 27, 2025, 7:56 p.m.

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Tara3

1 month, 2 weeks ago

Selected Answer: C

My answer is C AutoML Tables automatically builds and trains machine learning models based on your data, requiring minimal manual effort in model selection and hyperparameter tuning.    It offers built-in explainability features, including Shapley values, which provide a mathematically sound way to understand the contribution of each feature to the model's prediction.    AutoML Tables significantly reduces operational overhead by automating model building and providing explainability without requiring custom code or complex configurations.

upvoted 1 times

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ricardovazz

1 month, 2 weeks ago

Selected Answer: B

Between B and C, the question essentially comes down to: Do you need the flexibility to customize your ML approach beyond what AutoML provides? For most banking applications, the answer is yes.

upvoted 1 times

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CassiniExam

5 months, 2 weeks ago

Selected Answer: C

Considering the requirements of transparency, understandability, and minimal operational overhead, C. Use AutoML Tables with built-in explainability features, and use Shapley values for explainability. is the best option. It leverages a managed service with built-in explainability, providing a scalable and low-maintenance solution.

upvoted 2 times

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