A retail chain wants to forecast weekly sales for each of its 500 stores for the next 12 weeks. They have historical weekly sales data for each store, along with store-specific features like store size, location, and marketing spend. They want to use BigQuery ML to generate forecasts for all stores simultaneously. Which approach should they use?

Use ARIMA_PLUS_XREG with store ID as a categorical feature and store-specific features as regressors

Use a single ARIMA_PLUS model with a time series column and a series ID column to forecast all stores at once

Train a separate ARIMA_PLUS model for each store using a loop

Train a single ARIMA_PLUS model on the aggregated sales data across all stores

A retail company wants to predict whether a customer will churn in the next 30 days based on historical purchase behavior, account age, and support ticket history. The target variable is a binary flag (churned = 1, not churned = 0). The data science team wants to use BigQuery ML to train the model directly on data already stored in BigQuery. Which BigQuery ML model type should they use?

Logistic regression (CREATE MODEL ... OPTIONS(model_type='LOGISTIC_REG'))

Time series (CREATE MODEL ... OPTIONS(model_type='ARIMA_PLUS'))

K-means clustering (CREATE MODEL ... OPTIONS(model_type='KMEANS'))

Linear regression (CREATE MODEL ... OPTIONS(model_type='LINEAR_REG'))

In BigQuery ML, which approach is a valid method for feature selection when building a model?

Using ML.PREDICT to filter features

Using ML.EVALUATE to choose features

Manually selecting relevant columns in the SELECT statement

Including all available columns in the training query

A logistics company wants to predict the delivery time (in hours) for packages based on distance, package weight, and shipping method. The target variable is a continuous value. They have historical delivery data in BigQuery. Which BigQuery ML model type should they use?

Logistic regression (model_type='LOGISTIC_REG')

Linear regression (model_type='LINEAR_REG')

ARIMA_PLUS (model_type='ARIMA_PLUS')

K-means clustering (model_type='KMEANS')

Google CloudProfessional Machine Learning Engineer

Domain 1Objective 1

Developing ML Models Using BigQuery ML or AutoML on Gemini Enterprise Agent Platform PROFESSIONAL-MACHINE-LEARNING-ENGINEER Practice Questions (Page 1)

Part of the Architecting low-code AI solutions domain, which accounts for ~13% of the PROFESSIONAL-MACHINE-LEARNING-ENGINEER exam.

26questions here

6free pages

10concepts

~13%of the exam

10 Concepts in This Objective

Selecting BigQuery ML model types
Building classification models in BigQuery ML
Building regression models in BigQuery ML
Building forecasting models in BigQuery ML
Building clustering models in BigQuery ML
Performing feature engineering in BigQuery ML
Performing feature selection in BigQuery ML
Generating predictions with BigQuery ML
Training models with Agent Platform AutoML
Fine-tuning Gemini models using BigQuery

Questions 1–5

1expert · hard
A retail chain wants to forecast weekly sales for each of its 500 stores for the next 12 weeks. They have historical weekly sales data for each store, along with store-specific features like store size, location, and marketing spend. They want to use BigQuery ML to generate forecasts for all stores simultaneously. Which approach should they use?
Select an answer first
2application · medium
A retail company wants to predict whether a customer will churn in the next 30 days based on historical purchase behavior, account age, and support ticket history. The target variable is a binary flag (churned = 1, not churned = 0). The data science team wants to use BigQuery ML to train the model directly on data already stored in BigQuery. Which BigQuery ML model type should they use?
Select an answer first
3foundation · easy
In BigQuery ML, which approach is a valid method for feature selection when building a model?
Select an answer first
4application · medium
A logistics company wants to predict the delivery time (in hours) for packages based on distance, package weight, and shipping method. The target variable is a continuous value. They have historical delivery data in BigQuery. Which BigQuery ML model type should they use?
Select an answer first
5foundation · easy
A company wants to forecast daily website traffic for the next 30 days using historical daily data. Which BigQuery ML model type is most appropriate?
Select an answer first

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Developing ML Models Using BigQuery ML or AutoML on Gemini Enterprise Agent Platform PROFESSIONAL-MACHINE-LEARNING-ENGINEER Practice Questions (Page 1)

Questions 1–5

In BigQuery ML, which approach is a valid method for feature selection when building a model?

A logistics company wants to predict the delivery time (in hours) for packages based on distance, package weight, and shipping method. The target variable is a continuous value. They have historical delivery data in BigQuery. Which BigQuery ML model type should they use?

A company wants to forecast daily website traffic for the next 30 days using historical daily data. Which BigQuery ML model type is most appropriate?

Keep studying — Architecting low-code AI solutions