PMLE Architecting Low-Code ML Solutions Practice Question
A data scientist needs to forecast daily sales for the next 30 days using historical sales data stored in BigQuery. They want to use BigQuery ML. Which model type should they choose?
⚠ Common exam trap
Many candidates confuse regression models (like LINEAR_REG or BOOSTED_TREE_REGRESSOR) with time-series forecasting, not realizing that standard regression assumes independent observations and cannot inherently model temporal dependencies or extrapolate beyond the training period.
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 specifically designed for time-series forecasting, such as predicting daily sales over a future horizon. BigQuery ML's ARIMA_PLUS model automatically handles seasonality, trend, and holiday effects, making it ideal for 30-day sales forecasts from historical data.
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_REG
Why it's wrong here
LINEAR_REG fits a straight-line relationship between features and a numeric label, so it cannot capture the trend and seasonality of daily sales. It tempts because it is the simplest BigQuery ML regression model, and would be correct for forecasting a target that varies linearly with its inputs.
- ✗
BOOSTED_TREE_REGRESSOR
Why it's wrong here
BOOSTED_TREE_REGRESSOR predicts a single numeric label from input features and has no time dimension, so it cannot project sales forward 30 days. It tempts because it is a strong regression model, and would be correct for predicting sales from static features rather than forecasting over time.
- ✗
K_MEANS
Why it's wrong here
K_MEANS performs unsupervised clustering, assigning rows to groups without predicting a future numeric value, so it cannot produce 30-day sales forecasts. It tempts because it is a familiar BigQuery ML model, and would be correct for segmenting customers or products into similar groups.
- ✓
ARIMA_PLUS
Why this is correct
ARIMA_PLUS handles time-series forecasting natively in BigQuery ML, modelling trend, seasonality and holidays directly from historical data. It satisfies the requirement to forecast the next 30 days of daily sales without exporting data, and supports the forecast horizon through its horizon parameter.
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Same concept, more angles
3 more ways this is tested on PMLE
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A retail company wants to forecast weekly sales for each of its 500 stores. The data includes historical sales, promotions, holidays, and local weather. The company needs to update forecasts every week with new data. Which ML approach should they use?
easy- A.Use BigQuery ML to create a linear regression model on historical data
- ✓ B.Use Vertex AI Forecasting to train a time-series model with holiday and weather features
- C.Export data to AutoML Tables and train a regression model
- D.Build a custom LSTM model using TensorFlow on Vertex AI Workbench
Why B: Vertex AI Forecasting is purpose-built for time-series forecasting with support for exogenous features like holidays and weather, making it the ideal choice for weekly sales predictions across 500 stores. It handles multiple time series automatically and integrates with the required weekly retraining cycle, unlike generic regression models that lack temporal awareness.
Variation 2. 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?
easy- A.Call a pre-built Google Cloud API for sales prediction
- B.Use a linear regression model in Vertex AI
- C.Use Vertex AI AutoML Tables with date as feature
- ✓ D.Use BigQuery ML to train an ARIMA_PLUS model
Why D: 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.
Variation 3. A data scientist needs to train a time-series forecasting model on historical sales data stored in BigQuery to predict future demand. The data has strong seasonal patterns. Which BigQuery ML model type should they use?
easy- A.MATRIX_FACTORIZATION
- B.BOOSTED_TREE_REGRESSOR
- ✓ C.ARIMA_PLUS
- D.K_MEANS
Why C: ARIMA_PLUS is the correct choice because it is specifically designed for time-series forecasting in BigQuery ML, handling seasonal patterns, trend decomposition, and automatic hyperparameter tuning. It models autoregressive (AR) and moving average (MA) components with seasonal differencing, making it ideal for historical sales data with strong seasonal cycles.
JA
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