PMLE Architecting Low-Code ML Solutions Practice Question
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?
⚠ Common exam trap
This question 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.
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.
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.
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
The Natural Language API performs entity and sentiment extraction on unstructured text, not churn classification from tabular purchase data, so it cannot produce the required predictive model. It is tempting because it analyses support interactions, which are one input feature, but BigQuery ML handles the full SQL-based prediction in place.
- ✗
Export the data to a CSV file and use Vertex AI AutoML Tables to train a classification model.
Why it's wrong here
Exporting to CSV and training in AutoML Tables moves the data out of BigQuery, violating the requirement to avoid a separate environment. AutoML Tables is genuinely useful for tabular classification when teams lack SQL or ML expertise, but here BigQuery ML trains directly on the stored data.
- ✓
Use BigQuery ML to create a logistic regression model (LOGISTIC_REG) on the data directly in BigQuery.
Why this is correct
BigQuery ML trains LOGISTIC_REG models using SQL directly against BigQuery-resident data, so no extraction or separate environment is needed. This satisfies the team's SQL comfort and the constraint of avoiding data movement for churn prediction.
- ✗
Create a Dataflow pipeline to stream data to Cloud SQL and use Cloud SQL's built-in ML functions.
Why it's wrong here
Streaming BigQuery data through Dataflow into Cloud SQL adds a relational database and pipeline the team must operate, and Cloud SQL's ML functions cannot train churn models on that scale. It is tempting because Cloud SQL offers built-in prediction functions, which suit small operational tables rather than analytical training.
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 |
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Same concept, more angles
4 more ways this is tested on PMLE
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A data analyst wants to build a binary classification model to predict customer churn using SQL queries in BigQuery. Which BigQuery ML model type should they use?
easy- A.MATRIX_FACTORIZATION
- B.LINEAR_REG
- ✓ C.LOGISTIC_REG
- D.K_MEANS
Why C: LOGISTIC_REG is the BigQuery ML model type for binary classification, predicting a binary outcome such as churn (yes/no). It uses logistic regression to estimate the probability of the binary outcome. This is the correct choice for predicting customer churn.
Variation 2. A company needs to forecast product demand for the next 12 months using historical sales data. They want to use BigQuery ML with minimal coding. Which model type is most suitable?
medium- A.K_MEANS
- B.MATRIX_FACTORIZATION
- ✓ C.ARIMA_PLUS
- D.LINEAR_REG
Why C: ARIMA_PLUS is BigQuery ML's purpose-built time-series forecasting model, designed for exactly this scenario: forecasting future values from historical time-ordered data with minimal SQL coding. It automatically handles seasonality, holidays, and trend decomposition, and supports features like forecasting multiple time series at once. LINEAR_REG could technically model time as a feature, but it cannot capture seasonality or autocorrelation, making it unsuitable for demand forecasting.
Variation 3. 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?
easy- ✓ A.Use BigQuery ML with CREATE MODEL and LOGISTIC_REG model type.
- B.Use Cloud Datalab to train an XGBoost model on BigQuery data.
- C.Train a model using Vertex AI Workbench with a custom container.
- D.Export the data to Cloud Storage and use Vertex AI AutoML Tables.
Why A: 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.
Variation 4. A data analyst wants to train a linear regression model to predict house prices using only SQL queries on BigQuery. Which BigQuery ML model type should they use?
easy- A.BOOSTED_TREE_REGRESSOR
- B.LOGISTIC_REG
- ✓ C.LINEAR_REG
- D.DNN_REGRESSOR
Why C: The question specifies a linear regression model for predicting house prices, which is a regression task with a continuous target variable. BigQuery ML's LINEAR_REG model type is explicitly designed for linear regression, making it the correct choice for this use case.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.