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Exam Professional Machine Learning Engineer topic 1 question 89 discussion

Actual exam question from Google's Professional Machine Learning Engineer
Question #: 89
Topic #: 1
[All Professional Machine Learning Engineer Questions]

You work for a magazine distributor and need to build a model that predicts which customers will renew their subscriptions for the upcoming year. Using your company’s historical data as your training set, you created a TensorFlow model and deployed it to AI Platform. You need to determine which customer attribute has the most predictive power for each prediction served by the model. What should you do?

  • A. Use AI Platform notebooks to perform a Lasso regression analysis on your model, which will eliminate features that do not provide a strong signal.
  • B. Stream prediction results to BigQuery. Use BigQuery’s CORR(X1, X2) function to calculate the Pearson correlation coefficient between each feature and the target variable.
  • C. Use the AI Explanations feature on AI Platform. Submit each prediction request with the ‘explain’ keyword to retrieve feature attributions using the sampled Shapley method.
  • D. Use the What-If tool in Google Cloud to determine how your model will perform when individual features are excluded. Rank the feature importance in order of those that caused the most significant performance drop when removed from the model.
Show Suggested Answer Hide Answer
Suggested Answer: C 🗳️
by JeanEl at Dec. 13, 2022, 4:47 p.m.

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SubbuJV
Highly Voted 11 months, 3 weeks ago
Selected Answer: C
Vertex AI Explanations went with C
upvoted 5 times
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NamitSehgal
Most Recent 5 months, 2 weeks ago
Selected Answer: C
Feature Attributions for Individual Predictions Direct and Efficient
upvoted 1 times
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M25
1 year, 8 months ago
Selected Answer: C
Went with C
upvoted 2 times
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CloudKida
1 year, 8 months ago
Selected Answer: C
https://cloud.google.com/ai-platform/prediction/docs/ai-explanations/overview AI Explanations helps you understand your model's outputs for classification and regression tasks. Whenever you request a prediction on AI Platform, AI Explanations tells you how much each feature in the data contributed to the predicted result.
upvoted 1 times
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Yajnas_arpohc
1 year, 10 months ago
Key words in question "for each prediction served" - that make its C D is more of a broader analysis activity
upvoted 3 times
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John_Pongthorn
2 years ago
Selected Answer: C
You have to use a flagship native service as much as possible.
upvoted 1 times
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hiromi
2 years, 1 month ago
Selected Answer: D
I vote for D - https://www.tensorflow.org/tensorboard/what_if_tool - https://pair-code.github.io/what-if-tool/ - https://medium.com/red-buffer/tensorflows-what-if-tool-c52914ea215c C is wrong cuz AI Explanation dosen't work for TensorFlow models (https://cloud.google.com/vertex-ai/docs/explainable-ai/overview)
upvoted 1 times
mil_spyro
2 years, 1 month ago
This is from the doc you provided: "Feature attribution is supported for all types of models (both AutoML and custom-trained), frameworks (TensorFlow, scikit, XGBoost), and modalities (images, text, tabular, video)." https://cloud.google.com/vertex-ai/docs/explainable-ai/overview#supported_model_types_2
upvoted 3 times
hiromi
2 years, 1 month ago
Sorry, i tink C is the answer. Tks
upvoted 2 times
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hiromi
2 years, 1 month ago
Sorry, I mean Shapley method doesn't support TensorFlow Models See https://cloud.google.com/vertex-ai/docs/explainable-ai/overview#compare-methods
upvoted 1 times
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hiromi
2 years, 1 month ago
Sorry, i tink C is the answer
upvoted 3 times
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mil_spyro
2 years, 1 month ago
Selected Answer: C
AI Explanations provides feature attributions using the sampled Shapley method, which can help you understand how much each feature contributes to a model's prediction.
upvoted 4 times
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ares81
2 years, 1 month ago
Selected Answer: C
AI Explanations helps you understand your model's outputs for classification and regression tasks. Whenever you request a prediction on AI Platform, AI Explanations tells you how much each feature in the data contributed to the predicted result." It's C!
upvoted 2 times
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JeanEl
2 years, 1 month ago
Selected Answer: C
Agree with C
upvoted 2 times
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C (25%)
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