Question 556 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 company wants to build a model to predict housing prices using BigQuery ML. They have a dataset with features like area, number of bedrooms, and location. Which TWO model types are appropriate for this regression task?
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
✓
BOOSTED_TREE_REGRESSOR
BOOSTED_TREE_REGRESSOR (D) is appropriate because it is a tree-based ensemble method specifically designed for regression tasks, and BigQuery ML supports it via the `CREATE MODEL` statement with `model_type='BOOSTED_TREE_REGRESSOR'`. It handles non-linear relationships and interactions between features like area, bedrooms, and location, making it suitable for predicting continuous housing prices.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the distinction between regression and classification models, leading candidates to mistakenly choose LOGISTIC_REG for regression tasks because of the word 'regression' in its name, but it is actually a classification algorithm.
Detailed technical explanation
How to think about this question
In BigQuery ML, BOOSTED_TREE_REGRESSOR uses gradient boosting to sequentially build decision trees, minimizing loss (e.g., squared error) for regression. It automatically handles feature scaling and missing values, which is critical for real-world housing datasets with varied feature ranges. A subtle behavior is that it can overfit if the number of trees is too high, so hyperparameters like `max_tree_depth` and `learn_rate` must be tuned via `OPTIONS` in the CREATE MODEL statement.
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
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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: BOOSTED_TREE_REGRESSOR — BOOSTED_TREE_REGRESSOR (D) is appropriate because it is a tree-based ensemble method specifically designed for regression tasks, and BigQuery ML supports it via the `CREATE MODEL` statement with `model_type='BOOSTED_TREE_REGRESSOR'`. It handles non-linear relationships and interactions between features like area, bedrooms, and location, making it suitable for predicting continuous housing prices.
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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