PDE Preparing and Using Data for Analysis Practice Question
You are building a forecasting model to predict daily sales for the next 90 days using historical sales data with clear seasonality and trend. You want to use BigQuery ML with minimal manual tuning. Which model type should you choose?
⚠ Common exam trap
PDE often tests the misconception that any time-series model (like ARIMA) automatically handles seasonality and trend, but only ARIMA_PLUS in BigQuery ML offers automated preprocessing and multiple seasonality detection, so candidates may incorrectly choose standard ARIMA or a non-time-series model like XGBoost.
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 the correct choice because it is a Google-developed, automated time-series model in BigQuery ML that handles seasonality, trend, holidays, and outliers with minimal manual tuning. It automatically performs preprocessing such as missing value imputation, holiday effect detection, and seasonality decomposition, making it ideal for forecasting daily sales over a 90-day horizon. Unlike standard ARIMA, ARIMA_PLUS requires no manual specification of p, d, q parameters or seasonal orders, aligning with the 'minimal manual tuning' requirement.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
ARIMA
Why it's wrong here
ARIMA in BigQuery ML requires manual specification of non-seasonal and seasonal orders, contradicting the minimal-tuning requirement. It is tempting because ARIMA is designed for time series with trend and seasonality, and would be correct if you were willing to tune p, d, q and seasonal parameters yourself.
- ✗
Boosted tree (XGBoost)
Why it's wrong here
Boosted trees cannot extrapolate beyond the range of training targets, so they fail to project trend forward across 90 days. They are tempting because XGBoost excels at tabular classification and regression with engineered features, but forecasting with seasonality needs a model that models trend and seasonality explicitly.
- ✓
ARIMA_PLUS
Why this is correct
ARIMA_PLUS automatically handles seasonality, trend and holiday effects, and performs automatic model selection and hyperparameter tuning within BigQuery ML. This satisfies the minimal manual tuning constraint while producing accurate 90-day daily sales forecasts from historical data.
- ✗
Linear regression
Why it's wrong here
Linear regression fits a single straight-line relationship and cannot capture recurring seasonal patterns in the sales data. It is tempting because it is simple and handles trend, and would suit a dataset with a purely linear trend and no cyclical component, but seasonality here demands a seasonal model.
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Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
This PDE 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 PDE exam.