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Exam Professional Machine Learning Engineer topic 1 question 191 discussion

Actual exam question from Google's Professional Machine Learning Engineer
Question #: 191
Topic #: 1
[All Professional Machine Learning Engineer Questions]

You work for a retail company. You have created a Vertex AI forecast model that produces monthly item sales predictions. You want to quickly create a report that will help to explain how the model calculates the predictions. You have one month of recent actual sales data that was not included in the training dataset. How should you generate data for your report?

  • A. Create a batch prediction job by using the actual sales data. Compare the predictions to the actuals in the report.
  • B. Create a batch prediction job by using the actual sales data, and configure the job settings to generate feature attributions. Compare the results in the report.
  • C. Generate counterfactual examples by using the actual sales data. Create a batch prediction job using the actual sales data and the counterfactual examples. Compare the results in the report.
  • D. Train another model by using the same training dataset as the original, and exclude some columns. Using the actual sales data create one batch prediction job by using the new model and another one with the original model. Compare the two sets of predictions in the report.
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Suggested Answer: B 🗳️
by pikachu007 at Jan. 13, 2024, 3:47 a.m.

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fitri001
6 months, 1 week ago
Selected Answer: B
Feature Attribution: By enabling feature attributions in the batch prediction job, you gain insights into how each feature in the actual sales data contributes to the model's predictions. This information is crucial for explaining the model's reasoning to non-technical audiences. Direct Model Insights: Analyzing the feature attributions allows you to demonstrate how the model uses historical trends, seasonality, and other factors (represented by features) to predict future sales.
upvoted 2 times
fitri001
6 months, 1 week ago
A. Prediction vs. Actuals: While comparing predictions to actuals can be informative, it doesn't directly explain how the model arrives at those predictions. C. Counterfactual Examples: Counterfactuals can be useful for understanding model behavior, but creating them requires additional effort and might not be necessary for explaining the basic prediction process. D. Training a New Model: Training another model is time-consuming and unnecessary. Feature attributions provide valuable insights without needing a separate model.
upvoted 2 times
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MultiCloudIronMan
6 months, 3 weeks ago
Selected Answer: B
B is the best answer but am unsure why the report has to be compared with actual sales
upvoted 1 times
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ddogg
8 months, 4 weeks ago
Selected Answer: B
B) Will actually give you the information needed with Feature Attributions. E.g. The importance of each feature influencing the predictions sales items.
upvoted 3 times
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pikachu007
9 months, 2 weeks ago
Selected Answer: B
Feature attributions explicitly measure how much each input feature contributed to each prediction, providing the most relevant insights for understanding model behavior.
upvoted 2 times
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