Question 722 of 1,000
PMLE Architecting Low-Code ML Solutions Practice Question
This PMLE practice question tests your understanding of architecting low-code ml solutions. Examine the command output carefully: the correct answer depends on what the output actually shows, not on general recall alone. 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 retail company wants to build a recommendation system to show 'frequently bought together' items. 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
Option B is correct because 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.
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 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.
Detailed technical explanation
How to think about this question
Under the hood, the 'frequently-bought-together' model uses a co-occurrence matrix built from historical transaction data, where each cell represents the number of times two items appear in the same order. The model applies a confidence threshold (e.g., lift > 1) to filter out spurious associations, and can be tuned with parameters like minimum co-occurrence count to balance precision and recall. In a real-world scenario, this model is critical for e-commerce checkout pages to increase average order value by suggesting complementary items (e.g., a phone case with a phone), but it may fail for new items with no purchase history (cold-start problem).
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 company's IT admin needs to give a contractor read-only access to production logs without sharing account credentials. Using role-based access control (RBAC) and temporary scoped permissions — not a permanent shared password — is the correct pattern. Questions like this test whether you can apply least-privilege access across cloud identity services.
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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 — Option B is correct because 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.
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
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