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Google Professional Machine Learning Engineer Exam - Topic 5 Question 113 Discussion

You work for a retail company. You have been asked to develop a model to predict whether a customer will purchase a product on a given day. Your team has processed the company's sales data, and created a table with the following rows:* Customer_id* Product_id* Date* Days_since_last_purchase (measured in days)* Average_purchase_frequency (measured in 1/days)* Purchase (binary class, if customer purchased product on the Date)You need to interpret your models results for each individual prediction. What should you do?
B) Create a Vertex Al tabular dataset Train an AutoML model to predict customer purchases Deploy the model to a Vertex Al endpoint and enable feature attributions Use the 'explain' method to get feature attribution values for each individual prediction.
A) Create a BigQuery table Use BigQuery ML to build a boosted tree classifier Inspect the partition rules of the trees to understand how each prediction flows through the trees.
C) Create a BigQuery table Use BigQuery ML to build a logistic regression classification model Use the values of the coefficients of the model to interpret the feature importance with higher values corresponding to more importance.
D) Create a Vertex Al tabular dataset Train an AutoML model to predict customer purchases Deploy the model to a Vertex Al endpoint. At each prediction enable L1 regularization to detect non-informative features.

Google Professional Machine Learning Engineer Exam - Topic 5 Question 113 Discussion

Actual exam question for Google's Professional Machine Learning Engineer exam
Question #: 113
Topic #: 5
[All Professional Machine Learning Engineer Questions]

You work for a retail company. You have been asked to develop a model to predict whether a customer will purchase a product on a given day. Your team has processed the company's sales data, and created a table with the following rows:

* Customer_id

* Product_id

* Date

* Days_since_last_purchase (measured in days)

* Average_purchase_frequency (measured in 1/days)

* Purchase (binary class, if customer purchased product on the Date)

You need to interpret your models results for each individual prediction. What should you do?

Show Suggested Answer Hide Answer
Suggested Answer: B

According to the official exam guide1, one of the skills assessed in the exam is to ''explain the predictions of a trained model''.Vertex AI provides feature attributions using Shapley Values, a cooperative game theory algorithm that assigns credit to each feature in a model for a particular outcome2. Feature attributions can help you understand how the model calculates the predictions and debug or optimize the model accordingly.You can use AutoML for Tabular Data to generate and query local feature attributions3. The other options are not relevant or optimal for this scenario.Reference:

Professional ML Engineer Exam Guide

Feature attributions for classification and regression

AutoML for Tabular Data

Google Professional Machine Learning Certification Exam 2023

Latest Google Professional Machine Learning Engineer Actual Free Exam Questions


by Noble at Mar 31, 2026, 06:55 AM

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Ashlee
27 minutes ago
C is solid too. Logistic regression is interpretable.
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Jaclyn
5 days ago
I prefer option A. Boosted trees give great insights.
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Nelida
10 days ago
I think option B is the best. AutoML is powerful.
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Dominic
16 days ago
Not sure about L1 regularization; it might overlook some useful features.
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My
21 days ago
Definitely prefer the feature attribution method for clarity.
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Cherrie
26 days ago
Surprised that AutoML is an option here, seems too easy!
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Luann
2 months ago
I think logistic regression is more straightforward for interpretation.
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Vallie
3 months ago
A boosted tree classifier can give great insights!
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Precious
3 months ago
I recall that enabling L1 regularization helps with feature selection, but I'm not clear on how that would help interpret individual predictions in this case.
upvoted 0 times
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Chantell
3 months ago
I feel like logistic regression might be simpler for interpreting coefficients, but I'm not confident about how to relate those coefficients to actual feature importance in this context.
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Lenna
4 months ago
I think using AutoML with feature attributions sounds promising, especially since it allows for detailed explanations of predictions. I practiced a similar question on feature importance last week.
upvoted 0 times
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Edelmira
4 months ago
I remember we discussed how boosted tree classifiers can provide insights into individual predictions, but I'm not entirely sure how to interpret the partition rules effectively.
upvoted 0 times
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