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?

ACreate 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.

BCreate 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.

CCreate 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.

DCreate 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.

Show Suggested 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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2 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.

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