Question 362 of 990
mediumMultiple ChoiceObjective-mapped
Building Recommendations with BigQuery ML Matrix Factorization
A retail company wants to build a product recommendation system using BigQuery ML for their e-commerce platform. The data includes customer purchase history, product metadata, and clickstream logs. The ML engineer needs to minimize manual feature engineering and leverage pre-built solutions. Which approach should the engineer take?
Quick Answer
The correct answer is to use BigQuery ML’s matrix factorization model (model_type='matrix_factorization') because it is purpose-built for building recommendations directly from historical interaction data like customer purchases or clickstream logs, requiring no manual feature engineering. This approach trains on implicit or explicit feedback in a single SQL statement, automatically learning latent user and item factors to predict preferences—ideal for the retail e-commerce scenario described. On the Google Professional Machine Learning Engineer exam, this question tests your ability to match a pre-built BigQuery ML solution to a specific business problem, with a common trap being to overcomplicate by suggesting custom feature engineering or deep learning when a simpler, scalable option exists. The key insight is that matrix factorization handles sparse user-item matrices natively, so remember: “No features, just feedback—matrix factorization is the track.”
⚠ Common exam trap
Many candidates assume Vertex AI Model Garden (Option A) is the go-to for pre-built ML, but it does not offer a pre-trained recommendation model that can be directly deployed without custom training on the company's data.
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's matrix factorization model (CREATE MODEL with model_type='matrix_factorization') to train directly on historical interaction data.
BigQuery ML's matrix factorization model (model_type='matrix_factorization') is purpose-built for recommendation systems using implicit or explicit feedback data. It trains directly on historical interaction data (e.g., user-item purchases) without requiring manual feature engineering, aligning with the goal of minimizing low-code ML effort. This approach leverages BigQuery's native SQL interface and scales automatically, making it ideal for the described e-commerce scenario.
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 a pre-built recommendation model from Vertex AI Model Garden and deploy it to an endpoint.
Why it's wrong here
This is not a BigQuery ML solution and requires additional infrastructure.
- ✗
Write a custom TensorFlow model using the Vertex AI Training service and deploy it via Vertex AI Prediction.
Why it's wrong here
This requires significant custom coding, not low-code.
- ✗
Export the data to CSV and use AutoML Tables to train a recommendation model.
Why it's wrong here
AutoML Tables is not integrated with BigQuery ML; exporting data adds complexity.
- ✓
Use BigQuery ML's matrix factorization model (CREATE MODEL with model_type='matrix_factorization') to train directly on historical interaction data.
Why this is correct
BigQuery ML provides low-code matrix factorization for recommendations.
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 →
Same concept, more angles
1 more way 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 startup wants to build a product recommendation engine without writing custom training code. They have user-item interaction data stored in BigQuery. Which Google Cloud service should they use?
easy- A.Cloud Dataflow with ML APIs
- ✓ B.BigQuery ML matrix factorization
- C.Vertex AI AutoML Tables
- D.Vertex AI Matching Engine
Why B: BigQuery ML matrix factorization is the correct choice because it allows building a recommendation engine directly in BigQuery using SQL, without writing custom training code. It supports implicit and explicit user-item interaction data and provides built-in evaluation metrics, making it ideal for low-code ML solutions on existing BigQuery data.
Last reviewed: Jun 11, 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.