Question 312 of 1,000
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 retailer wants to implement a recommendation engine that suggests products based on a user's current cart. They have limited ML expertise and want a quick deployment. Which Recommendations AI model type should they use?
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 is the correct choice because it directly leverages collaborative filtering based on co-purchase patterns in historical transaction data, enabling the retailer to recommend items commonly purchased alongside the current cart contents. This model requires minimal ML expertise and can be quickly deployed using pre-built Recommendations AI templates, as it does not require user-level personalization or real-time session data.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Common exam traps
Common exam trap: answer the scenario, not the keyword
In Google PMLE exams, candidates often mistakenly select 'Recommended for you' thinking it is the default or most versatile model, but the trap here is that they overlook the specific requirement of 'based on a user's current cart' and the need for quick deployment with limited ML expertise, which points to the simpler, cart-focused 'Frequently bought together' model instead.
Detailed technical explanation
How to think about this question
Under the hood, 'Frequently bought together' typically uses association rule mining (e.g., the Apriori algorithm) or co-occurrence matrices to identify product pairs with high lift and support in transaction logs. In Google Cloud Recommendations AI, this model type is optimized for shopping cart scenarios and can be deployed via a simple API call without training custom models, making it ideal for rapid integration. A real-world scenario is an e-commerce site adding a 'Complete the look' section that suggests accessories for a dress in the cart, leveraging aggregated purchase data from thousands of users.
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.
What to study next
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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: Frequently bought together — The 'Frequently bought together' model type is the correct choice because it directly leverages collaborative filtering based on co-purchase patterns in historical transaction data, enabling the retailer to recommend items commonly purchased alongside the current cart contents. This model requires minimal ML expertise and can be quickly deployed using pre-built Recommendations AI templates, as it does not require user-level personalization or real-time session data.
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.
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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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