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Exam Professional Machine Learning Engineer All Questions

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Exam Professional Machine Learning Engineer topic 1 question 225 discussion

Actual exam question from Google's Professional Machine Learning Engineer
Question #: 225
Topic #: 1
[All Professional Machine Learning Engineer Questions]

You work for a telecommunications company. You’re building a model to predict which customers may fail to pay their next phone bill. The purpose of this model is to proactively offer at-risk customers assistance such as service discounts and bill deadline extensions. The data is stored in BigQuery and the predictive features that are available for model training include:

- Customer_id
- Age
- Salary (measured in local currency)
- Sex
- Average bill value (measured in local currency)
- Number of phone calls in the last month (integer)
- Average duration of phone calls (measured in minutes)

You need to investigate and mitigate potential bias against disadvantaged groups, while preserving model accuracy.

What should you do?

  • A. Determine whether there is a meaningful correlation between the sensitive features and the other features. Train a BigQuery ML boosted trees classification model and exclude the sensitive features and any meaningfully correlated features.
  • B. Train a BigQuery ML boosted trees classification model with all features. Use the ML.GLOBAL_EXPLAIN method to calculate the global attribution values for each feature of the model. If the feature importance value for any of the sensitive features exceeds a threshold, discard the model and tram without this feature.
  • C. Train a BigQuery ML boosted trees classification model with all features. Use the ML.EXPLAIN_PREDICT method to calculate the attribution values for each feature for each customer in a test set. If for any individual customer, the importance value for any feature exceeds a predefined threshold, discard the model and train the model again without this feature.
  • D. Define a fairness metric that is represented by accuracy across the sensitive features. Train a BigQuery ML boosted trees classification model with all features. Use the trained model to make predictions on a test set. Join the data back with the sensitive features, and calculate a fairness metric to investigate whether it meets your requirements.
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Suggested Answer: D 🗳️
by pikachu007 at Jan. 13, 2024, 7:27 a.m.

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fitri001
6 months, 1 week ago
Selected Answer: D
Fairness Metric: Defining a metric like parity (equal accuracy) or calibration (similar predicted probabilities) across sensitive features like age, sex, or salary allows you to quantify potential bias. Model Training with All Features (Initially): Training the model with all features provides a baseline performance and allows you to identify potentially biased features later. Test Set Predictions: Making predictions on a held-out test set ensures the evaluation is based on unseen data and avoids overfitting. Joining Back Sensitive Features: Reintroducing sensitive features after prediction allows you to calculate fairness metrics for different customer groups. Iterative Refinement: Based on the fairness metric results, you can determine if further mitigation strategies are needed.
upvoted 3 times
fitri001
6 months, 1 week ago
A. Excluding Features Based on Correlation: While correlated features might indicate bias, simply excluding them can discard valuable information and potentially reduce model accuracy. B. Global Attribution for Feature Removal: Using global feature importance might not reveal bias impacting specific customer groups. Additionally, discarding a feature solely based on importance could affect model performance. C. Individual Attribution for Model Discarding: While individual attribution can identify per-customer bias, discarding the model entirely based on a single instance might be overly cautious and lead to starting from scratch frequently.
upvoted 1 times
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pinimichele01
6 months, 3 weeks ago
Selected Answer: D
https://cloud.google.com/vertex-ai/docs/evaluation/intro-evaluation-fairness
upvoted 1 times
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prtikare
7 months, 2 weeks ago
Answer is A
upvoted 1 times
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shadz10
9 months, 2 weeks ago
Selected Answer: D
https://cloud.google.com/vertex-ai/docs/evaluation/intro-evaluation-fairness
upvoted 1 times
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pikachu007
9 months, 2 weeks ago
Selected Answer: D
Direct Bias Assessment: It directly measures model fairness using a relevant metric, providing clear insights into potential issues. Preserving Information: It avoids prematurely removing features, potentially capturing valuable predictive signals while mitigating bias. Aligning with Goals: It allows tailoring the fairness metric to specific ethical and business objectives.
upvoted 2 times
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