- A
MATRIX_FACTORIZATION
Why wrong: Matrix factorization is for recommendation systems.
- B
BOOSTED_TREE_REGRESSOR
Why wrong: Boosted tree regressor is for regression, not time-series forecasting.
- C
ARIMA_PLUS
ARIMA_PLUS is the correct model for time-series forecasting in BigQuery ML.
- D
K_MEANS
Why wrong: K_MEANS is for clustering, not forecasting.
Question 814 of 1,000
PMLE Architecting Low-Code ML Solutions Practice Question
This PMLE practice question tests your understanding of architecting low-code ml solutions. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
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 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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
MATRIX_FACTORIZATION
Why it's wrong here
Matrix factorization is for recommendation systems.
- ✗
BOOSTED_TREE_REGRESSOR
Why it's wrong here
Boosted tree regressor is for regression, not time-series forecasting.
- ✓
ARIMA_PLUS
Why this is correct
ARIMA_PLUS is the correct model for time-series forecasting in BigQuery ML.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
K_MEANS
Why it's wrong here
K_MEANS is for clustering, not forecasting.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that any regression model (like BOOSTED_TREE_REGRESSOR) can be naively applied to time-series data, ignoring the need for specialized models that handle temporal dependencies and seasonality natively.
Detailed technical explanation
How to think about this question
ARIMA_PLUS extends classical ARIMA by automatically detecting and modeling seasonality, holidays, and step changes via a time-series decomposition into trend, seasonal, and residual components. It uses a Kalman filter for state-space estimation and can handle multiple seasonal periods (e.g., daily, weekly, yearly) by fitting a separate seasonal ARIMA model for each frequency. In practice, this means the model can capture complex patterns like monthly sales spikes due to promotions or yearly holiday effects without manual intervention.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A company's IT admin needs to give a contractor read-only access to production logs without sharing account credentials. Using role-based access control (RBAC) and temporary scoped permissions — not a permanent shared password — is the correct pattern. Questions like this test whether you can apply least-privilege access across cloud identity services.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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FAQ
Questions learners often ask
What does this PMLE question test?
Architecting Low-Code ML Solutions — This question tests Architecting Low-Code ML Solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: ARIMA_PLUS — 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.
What should I do if I get this PMLE question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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.
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