Question 522 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 analyst wants to train a binary classification model on a BigQuery table without moving data out of BigQuery. They have limited ML expertise. Which approach should they take?
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
✓
Use BigQuery ML with CREATE MODEL and LOGISTIC_REG model type.
Option A is correct because BigQuery ML allows users to create and train binary classification models directly on data in BigQuery using SQL, with no need to move data or have deep ML expertise. The LOGISTIC_REG model type implements logistic regression, a standard algorithm for binary classification, and the CREATE MODEL statement handles all the underlying training infrastructure, making it ideal for a data analyst with limited ML skills.
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 low-code/no-code solutions (like BigQuery ML) and more advanced, infrastructure-heavy approaches (like custom containers or AutoML with data export), expecting candidates to recognize that the simplest, most integrated option is correct when the user has limited ML expertise and wants to avoid data movement.
Detailed technical explanation
How to think about this question
BigQuery ML's LOGISTIC_REG model type uses stochastic gradient descent (SGD) for optimization and supports regularization (L2 by default) to prevent overfitting. Under the hood, it leverages BigQuery's distributed processing to scale training across large datasets without requiring the user to manage compute resources. A real-world scenario is a marketing analyst building a churn prediction model on a 10TB customer event table—BigQuery ML can train the model in minutes using SQL, whereas other options would require data export and additional tooling.
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 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
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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: Use BigQuery ML with CREATE MODEL and LOGISTIC_REG model type. — Option A is correct because BigQuery ML allows users to create and train binary classification models directly on data in BigQuery using SQL, with no need to move data or have deep ML expertise. The LOGISTIC_REG model type implements logistic regression, a standard algorithm for binary classification, and the CREATE MODEL statement handles all the underlying training infrastructure, making it ideal for a data analyst with limited ML skills.
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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