PMLE Architecting Low-Code ML Solutions Practice Question
A retail company wants to implement a recommendation system using Recommendations AI. They need to generate personalized recommendations for users based on their browsing history and purchase behavior. Which THREE recommendation types are available in Recommendations AI?
⚠ Common exam trap
PMLE often tests the distinction between valid recommendation types in Recommendations AI and generic recommendation strategies like 'trending-now' or 'most-popular', which are not available as predefined types.
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
✓
recommended-for-you
Recommendations AI offers several built-in recommendation types, and recommended-for-you (B) is correct because it produces personalized product suggestions for a user based on that user's own browsing history, purchase behavior, and other interaction events. others-you-may-like (C) is also correct because it generates personalized recommendations related to a specific item the user is currently viewing, which fits the retail scenario of tailoring suggestions from user behavior. frequently-bought-together (D) is correct because it recommends complementary products commonly purchased alongside a given item, a standard Recommendations AI type for cross-sell use cases. The unmarked options do not belong: trending-now (A) and most-popular (E) are not valid Recommendations AI recommendation type identifiers, as the platform's types are named like recommended-for-you, others-you-may-like, frequently-bought-together, and similar-item, rather than those generic popularity labels.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
trending-now
Why it's wrong here
Recommendations AI offers recommended-for-you, others-you-may-like, frequently-bought-together, and similar-item types; trending-now is a Retail Search or Vertex AI Search feature, not a Recommendations AI type. It is tempting because trending content suits retail merchandising, but it does not personalise from a user's browsing and purchase history.
- ✓
recommended-for-you
Why this is correct
Recommended-for-you is a genuine Recommendations AI type that personalises suggestions per user from their browsing history and past behaviour. This directly matches the stem's requirement to generate personalised recommendations based on browsing and purchase activity.
- ✓
others-you-may-like
Why this is correct
Others-you-may-like is a genuine Recommendations AI type, generating related-item suggestions from co-occurrence patterns in browsing and purchase data. It satisfies the stem's requirement for personalised recommendations derived from user behaviour, alongside the other two valid types.
- ✓
frequently-bought-together
Why this is correct
Frequently-bought-together is a valid Recommendations AI type, generating item associations from co-purchase patterns across all users rather than per-user history. It satisfies the stem's requirement to name available recommendation types, alongside "recommended-for-you" and "others-you-may-like", which cover the personalised browsing and purchase behaviour signals described.
- ✗
most-popular
Why it's wrong here
Most-popular returns the same items to every user, ignoring the browsing history and purchase behaviour the scenario requires for personalisation. It is genuinely a Recommendations AI type, useful as a fallback when a user or item has insufficient interaction data for personalised models.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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.