Exam Professional Machine Learning Engineer topic 1 question 309 discussion

Actual exam question from Google's Professional Machine Learning Engineer

Question #: 309
Topic #: 1

[All Professional Machine Learning Engineer Questions]

You are developing a natural language processing model that analyzes customer feedback to identify positive, negative, and neutral experiences. During the testing phase, you notice that the model demonstrates a significant bias against certain demographic groups, leading to skewed analysis results. You want to address this issue following Google's responsible AI practices. What should you do?

A. Use Vertex AI's model evaluation lo assess bias in the model's predictions, and use post-processing to adjust outputs for identified demographic discrepancies.
B. Implement a more complex model architecture that can capture nuanced patterns in language to reduce bias.
C. Audit the training dataset to identify underrepresented groups and augment the dataset with additional samples before retraining the model.
D. Use Vertex Explainable AI to generate explanations and systematically adjust the predictions to address identified biases.

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Suggested Answer: C 🗳️

by hit_cloudie at May 21, 2025, 9 a.m.

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qaz09

1 month ago

Selected Answer: A

A -> here we are using Google's recommended tool for bias evaluation (https://cloud.google.com/vertex-ai/docs/evaluation/model-bias-metrics) B -> using more complex model does not address bias directly c -> manual work + we can not expect that there is an option to add more samples to training dataset d -> adjusting predictions is manual work

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spradhan

2 weeks, 5 days ago

Yes but we cannot adjust the output in post processing. Preprocessing mitigation would make sense. As per google https://developers.google.com/machine-learning/crash-course/fairness/mitigating-bias Bias can be mitigated by augmenting the data or getting more data or changing loss function

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hit_cloudie

2 months, 3 weeks ago

Selected Answer: C

Google's Responsible AI best practices prioritize dataset auditing and fairness through balanced representation. This helps address the root cause of the bias (biased training data).

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