PMLE Architecting Low-Code ML Solutions Practice Question
A retail company wants to forecast monthly sales for each of its 500 stores using historical sales data. They have two years of daily sales data per store and want to use BigQuery ML to build a forecasting model. They need to account for seasonality and trends. Which BigQuery ML model type should they use?
⚠ Common exam trap
The trap here is using a general regression model like linear regression for time series data without accounting for seasonality.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
ARIMA_PLUS
ARIMA_PLUS is specifically designed for time series forecasting in BigQuery ML. It automatically detects and models seasonality, trends, and holidays, which are critical for retail sales data. It supports multiple time series, allowing per-store forecasts. This low-code solution requires minimal feature engineering and provides accurate, explainable results, making it the right choice for the company's needs.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Linear regression
Why it's wrong here
Linear regression can model trends but does not inherently account for seasonality or complex temporal patterns. For time series forecasting with seasonality, a specialized model like ARIMA_PLUS is more appropriate. Linear regression would require manual feature engineering to capture seasonal effects, which is cumbersome and less accurate. Therefore, it is not the best choice for this scenario.
- ✗
Logistic regression
Why it's wrong here
Logistic regression is used for binary classification, not for forecasting continuous values like sales. It cannot predict numeric outcomes such as monthly sales figures. The retail company needs a regression or time series model, so logistic regression is fundamentally incorrect for this task.
- ✗
K-means clustering
Why it's wrong here
K-means is an unsupervised clustering algorithm and does not perform forecasting. It groups data points based on similarity, which is unrelated to predicting future sales. The company needs a predictive model that can extrapolate temporal patterns, so k-means is not applicable.
- ✓
ARIMA_PLUS
Why this is correct
ARIMA_PLUS is a built-in BigQuery ML model for time series forecasting that automatically handles seasonality, trends, and holidays. It is designed for forecasting multiple time series, such as sales per store, and can process large datasets. It provides explainable forecasts and requires only the time series identifier, timestamp, and value columns. This makes it ideal for the retail company's monthly sales forecasting across many stores.
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Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
This PMLE practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the PMLE exam.