PMLE Architecting Low-Code ML Solutions Practice Question
A retail company wants to build a recommendation system to show 'frequently bought together' items. Which Recommendations AI model type should they use?
⚠ Common exam trap
In Google Cloud Recommendations AI, the key distinction is between transaction-based co-purchase models ('frequently-bought-together') and personalized recommendation models ('recommended-for-you'). Candidates may confuse 'frequently-bought-together' with 'others-you-may-like' because both relate to item similarity, but only 'frequently-bought-together' uses co-occurrence analysis of actual transactions.
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
✓
frequently-bought-together
The 'frequently-bought-together' model type in Google Cloud Recommendations AI is specifically designed to identify items that are commonly purchased in the same transaction, using co-occurrence analysis of historical purchase data. This directly matches the requirement to show items that are frequently bought together, leveraging association rule mining (e.g., Apriori algorithm) to generate recommendations.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
recently-viewed
Why it's wrong here
Recently-viewed recommends items based on a user's own browsing history, so it cannot surface cross-product purchase pairings. It is tempting because it genuinely powers personalised 'recently viewed' carousels, but the stem requires co-purchase associations, which only the frequently-bought-together model derives from order data.
- ✓
frequently-bought-together
Why this is correct
The frequently-bought-together model type is purpose-built to surface item pairs or sets commonly purchased in the same transaction, matching the retail 'frequently bought together' requirement. Other model types target different goals such as personalised ranking or similar-item recommendations.
- ✗
recommended-for-you
Why it's wrong here
Recommended-for-you personalises a homepage-style list per shopper, so it cannot express a fixed product-pairing relationship. It is tempting because it is the general-purpose retail recommender, but the stem asks for basket co-occurrence, which the frequently-bought-together model supplies from order history.
- ✗
others-you-may-like
Why it's wrong here
Others-you-may-like predicts items similar to a seed product, not products statistically co-purchased with it. It is tempting because it does generate product-to-product recommendations, but its signal is similarity, whereas 'frequently bought together' requires the frequently-bought-together model trained on basket co-occurrence.
Go deeper
Related to this question
About these practice questions
One of 775 original PMLE practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. Learn why practice questions differ from exam dumps →
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.