The answer is that the recommendation model is not filtering out previously purchased items. This is the most likely cause because Einstein Recommendations relies on customer purchase history to generate personalized suggestions, but unless the model’s filtering logic is explicitly configured to exclude items a customer has already bought, those same products will continue to appear in the recommendations. On the Salesforce AI Associate exam, this scenario tests your understanding of recommendation model configuration versus data sync or algorithm type issues—a common trap is assuming the problem is a data lag when it is actually a missing exclusion rule. To remember this, think of it as a “purchase blind spot”: the model sees what was bought but doesn’t know to hide it unless told. A quick memory tip: “If it’s already in the cart, keep it out of the heart.”
AI Associate AI Fundamentals Practice Question
This AI Associate practice question tests your understanding of ai fundamentals. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 marketing manager uses Einstein recommendations on their website, but customers are receiving suggestions for products they already purchased. What is the most likely cause?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
The recommendation model is not filtering out previously purchased items.
C is correct because the most likely cause is that the recommendation model is not configured to exclude previously purchased items. Einstein Recommendations uses customer purchase history to personalize suggestions, but if the model's filtering logic does not explicitly remove items the customer has already bought, those items will continue to appear in the recommendations. This is a common oversight in model configuration rather than a data sync or algorithm type issue.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
The product catalog is not updated with purchase history.
Why it's wrong here
Purchase history is separate from catalog.
✗
The model is using real-time browsing data that includes past purchases.
Why it's wrong here
Real-time data does not include past purchases.
✓
The recommendation model is not filtering out previously purchased items.
Why this is correct
Einstein recommendations can exclude purchased items.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
The recommendations are based on collaborative filtering without personalization.
Why it's wrong here
Personalization considers purchase history.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Salesforce often tests the distinction between data source issues (e.g., catalog not updated) versus model configuration issues (e.g., missing filters), and the trap here is assuming the problem is a data sync failure when it is actually a missing business rule in the recommendation logic.
Detailed technical explanation
How to think about this question
Einstein Recommendations uses a combination of collaborative filtering and content-based filtering, but the model's output is governed by business rules and filters defined in the Einstein Recommendations configuration. A common filter is 'exclude items purchased in the last X days' or 'exclude items from order history.' If this filter is not applied, the model will return items with high affinity scores even if the customer already owns them, leading to poor user experience and reduced conversion rates.
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 practitioner preparing for the AI Associate exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
AI Fundamentals — This question tests AI Fundamentals — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: The recommendation model is not filtering out previously purchased items. — C is correct because the most likely cause is that the recommendation model is not configured to exclude previously purchased items. Einstein Recommendations uses customer purchase history to personalize suggestions, but if the model's filtering logic does not explicitly remove items the customer has already bought, those items will continue to appear in the recommendations. This is a common oversight in model configuration rather than a data sync or algorithm type issue.
What should I do if I get this AI Associate question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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 →
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 nonprofit uses Einstein Recommendations to suggest donations. They notice that the recommendations are not relevant. Which best practice should they follow to improve relevance?
medium
✓ A.Verify that the Recommendation object has enough historical interaction data and that events are correctly tracked.
B.Set a data retention policy to delete records older than 30 days to keep data fresh.
C.Increase the number of recommended items to 10 to give more choices.
D.Display recommendations on every page, including the donation receipt page.
Why A: Option B is correct because Einstein Recommendations depend on user interaction data; ensuring event tracking is accurate and sufficient is key. Option A is wrong because more options can lead to analysis paralysis. Option C is wrong because more recommendations per page can overwhelm users. Option D is wrong because data retention policies don't directly improve relevance.
Last reviewed: Jun 30, 2026
Question Discussion
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This AI Associate practice question is part of Courseiva's free Salesforce 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 AI Associate exam.
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