easyMultiple Choice
PMLE Practice Question: A retail company wants to forecast daily sales…
A retail company wants to forecast daily sales for inventory planning. They have 3 years of historical sales data with clear weekly and yearly seasonality. Which approach should they use?
⚠ Common exam trap
Test-takers frequently choose AutoML Tables (Option C) thinking it can handle any structured data, but they miss that AutoML Tables is not a dedicated time-series model and requires manual feature engineering to capture seasonality, whereas ARIMA_PLUS is purpose-built for this scenario.
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
✓
Use BigQuery ML to train an ARIMA_PLUS model
ARIMA_PLUS in BigQuery ML is specifically designed for time-series forecasting with multiple seasonalities (weekly and yearly). It automatically handles seasonality detection, trend decomposition, and holiday effects, making it ideal for retail sales data with clear periodic patterns.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Call a pre-built Google Cloud API for sales prediction
Why it's wrong here
A pre-built sales prediction API cannot learn the specific weekly and yearly seasonal patterns in three years of company data. Such APIs suit generic, low-volume forecasting; AutoML Tables or a custom model trained on the historical series captures the seasonality.
- ✗
Use a linear regression model in Vertex AI
Why it's wrong here
Linear regression fits a straight-line relationship between features and the target, so it cannot represent the recurring weekly and yearly cycles present in three years of sales history. It is tempting because regression suits trend or causal forecasting where seasonality is absent or already removed.
- ✗
Use Vertex AI AutoML Tables with date as feature
Why it's wrong here
AutoML Tables treats the date column as an ordinary numeric or categorical feature, so it cannot extrapolate future timestamps or model recurring seasonal cycles. It is tempting because AutoML Tables excels at tabular classification and regression on static datasets, not time-series forecasting.
- ✓
Use BigQuery ML to train an ARIMA_PLUS model
Why this is correct
ARIMA_PLUS in BigQuery ML handles time-series forecasting natively, automatically modelling weekly and yearly seasonality plus trend from the three years of historical data. This satisfies the stated seasonality requirement without manual feature engineering, and runs where the sales data already resides.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.