Quick Answer
The answer is regression, classification, and time-series forecasting. BigQuery ML supports these three supervised output types through distinct model architectures: `LINEAR_REG` for regression tasks predicting continuous values, `LOGISTIC_REG` for classification problems outputting discrete labels or probabilities, and `ARIMA_PLUS` for time-series forecasting that predicts future data points based on historical patterns. On the Google Professional Machine Learning Engineer exam, this concept tests your understanding of how BigQuery ML abstracts traditional ML workflows into SQL, with a common trap being the assumption that clustering or unsupervised methods are supported as primary output types—they are not, as BigQuery ML focuses on supervised and forecasting tasks. A useful memory tip is to think of the three core business problems: "How much?" (regression), "Which category?" (classification), and "What's next?" (forecasting).
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.
Which THREE of the following are supported output types for BigQuery ML?
Classification
e.g., logistic regression model.
Object detection
Why wrong: Not supported.
Anomaly detection
Why wrong: Not an official model type.
Time-series forecasting
e.g., ARIMA model.
Regression
e.g., linear regression model.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Classification
BigQuery ML supports supervised learning tasks like classification and regression, as well as time-series forecasting, through its model types such as `LOGISTIC_REG`, `LINEAR_REG`, and `ARIMA_PLUS`. Classification (option A) is correct because BigQuery ML provides `LOGISTIC_REG` for binary and multi-class classification problems, outputting predicted labels or probabilities.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Classification
Why this is correct
e.g., logistic regression model.
Related concept
Read the scenario before looking for a memorised answer.
Object detection
Why it's wrong here
Not supported.
Anomaly detection
Why it's wrong here
Not an official model type.
Time-series forecasting
Why this is correct
e.g., ARIMA model.
Related concept
Read the scenario before looking for a memorised answer.
Regression
Why this is correct
e.g., linear regression model.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Google Cloud often tests the distinction between supported BigQuery ML output types and broader ML capabilities, leading candidates to mistakenly include object detection or anomaly detection, which are not native output types in BigQuery ML's SQL-based interface.
Detailed technical explanation
BigQuery ML leverages SQL-based model creation and inference, with supported model types including `LINEAR_REG` for regression, `LOGISTIC_REG` for classification, and `ARIMA_PLUS` for time-series forecasting. Under the hood, these models are trained using distributed TensorFlow or XGBoost, and outputs are generated as SQL query results, making it easy to integrate with existing data pipelines. A real-world scenario is predicting customer churn (classification) or sales trends (forecasting) directly within BigQuery without moving data.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Architecting low-code ML solutions — study guide chapter
Learn the concepts, then practise the questions
Architecting low-code ML solutions practice questions
Targeted practice on this topic area only
All PMLE questions
506 questions across all exam domains
Google Professional Machine Learning Engineer study guide
Full concept coverage aligned to exam objectives
PMLE practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Practise PMLE questions linked to Scaling prototypes into ML models.
Practise PMLE questions linked to Automating and orchestrating ML pipelines.
Practise PMLE questions linked to Collaborating within and across teams to manage data and models.
Practise PMLE questions linked to Architecting low-code ML solutions.
Practise PMLE questions linked to Collaborating to manage data and models.
Practise PMLE questions linked to Serving and scaling models.
Practise PMLE questions linked to Monitoring ML solutions.
Practise PMLE questions linked to Solving business challenges with ML.
Practise PMLE questions linked to PMLE fundamentals.
Practise PMLE questions linked to PMLE scenario.
Practise PMLE questions linked to PMLE troubleshooting.
Practice this exam
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
FAQ
Architecting low-code ML solutions — This question tests Architecting low-code ML solutions — Read the scenario before looking for a memorised answer..
The correct answer is: Classification — BigQuery ML supports supervised learning tasks like classification and regression, as well as time-series forecasting, through its model types such as `LOGISTIC_REG`, `LINEAR_REG`, and `ARIMA_PLUS`. Classification (option A) is correct because BigQuery ML provides `LOGISTIC_REG` for binary and multi-class classification problems, outputting predicted labels or probabilities.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Last reviewed: Jun 30, 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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.