- A
Use the Cloud Natural Language API to analyze customer support interactions and combine results with purchase data in BigQuery.
Why wrong: This does not build a predictive churn model; it only analyzes text.
- B
Export the data to a CSV file and use Vertex AI AutoML Tables to train a classification model.
Why wrong: This requires moving data out of BigQuery, which the scenario wants to avoid.
- C
Use BigQuery ML to create a logistic regression model (LOGISTIC_REG) on the data directly in BigQuery.
BigQuery ML supports logistic regression for binary classification and runs entirely in BigQuery using SQL.
- D
Create a Dataflow pipeline to stream data to Cloud SQL and use Cloud SQL's built-in ML functions.
Why wrong: Cloud SQL does not have built-in ML functions like BigQuery ML.
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 retail company wants to predict customer churn using historical purchase data stored in BigQuery. The data includes customer demographics, transaction history, and support interactions. The team is comfortable writing SQL and wants to avoid moving data to a separate environment. 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 to create a logistic regression model (LOGISTIC_REG) on the data directly in BigQuery.
Option C is correct because BigQuery ML allows the team to build and train a logistic regression model directly on data stored in BigQuery using SQL syntax, without moving data to a separate environment. The LOGISTIC_REG model type is specifically designed for binary classification tasks like churn prediction, and it runs entirely within BigQuery's serverless infrastructure, satisfying the team's requirement to avoid data movement.
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.
- ✗
Use the Cloud Natural Language API to analyze customer support interactions and combine results with purchase data in BigQuery.
Why it's wrong here
This does not build a predictive churn model; it only analyzes text.
- ✗
Export the data to a CSV file and use Vertex AI AutoML Tables to train a classification model.
Why it's wrong here
This requires moving data out of BigQuery, which the scenario wants to avoid.
- ✓
Use BigQuery ML to create a logistic regression model (LOGISTIC_REG) on the data directly in BigQuery.
Why this is correct
BigQuery ML supports logistic regression for binary classification and runs entirely in BigQuery using SQL.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Create a Dataflow pipeline to stream data to Cloud SQL and use Cloud SQL's built-in ML functions.
Why it's wrong here
Cloud SQL does not have built-in ML functions like BigQuery ML.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that ML requires moving data to a separate platform (like Vertex AI or Cloud SQL), when in fact BigQuery ML provides a low-code, SQL-based solution that keeps data in place and meets the stated constraints.
Trap categories for this question
Scenario analysis trap
This requires moving data out of BigQuery, which the scenario wants to avoid.
Detailed technical explanation
How to think about this question
BigQuery ML's LOGISTIC_REG model uses stochastic gradient descent (SGD) for optimization and supports regularization (L1/L2) to prevent overfitting. Under the hood, it transforms the SQL query into a distributed training job that runs across BigQuery's slots, enabling training on terabytes of data without manual infrastructure management. A real-world scenario is a retail company with millions of transactions in BigQuery that can run `CREATE MODEL` with a `SELECT` statement to train a churn model in minutes, then use `ML.PREDICT` to score new customers in the same environment.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
What to study next
Got this wrong? Here's your next step.
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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 to create a logistic regression model (LOGISTIC_REG) on the data directly in BigQuery. — Option C is correct because BigQuery ML allows the team to build and train a logistic regression model directly on data stored in BigQuery using SQL syntax, without moving data to a separate environment. The LOGISTIC_REG model type is specifically designed for binary classification tasks like churn prediction, and it runs entirely within BigQuery's serverless infrastructure, satisfying the team's requirement to avoid data movement.
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.
About these practice questions
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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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