The answer is to increase recall to reduce false negatives. This is correct because the precision-recall trade-off in lead scoring means a model with 0.9 precision and 0.5 recall is highly selective but misses half of actual leads, creating costly false negatives—lost sales opportunities that outweigh the cost of false positives. On the Salesforce AI Associate exam, this scenario tests your understanding of how business context drives metric priorities: in lead scoring, recall is typically more critical than precision because missing a potential customer directly impacts revenue. A common trap is assuming high precision alone is ideal, but the exam emphasizes that the trade-off must align with the use case’s cost structure. Memory tip: for lead scoring, think “Recall Recovers Revenue”—if you miss leads, you miss money, so prioritize recall over precision when false negatives are the greater risk.
AI Associate AI Fundamentals Practice Question
This AI Associate practice question tests your understanding of ai fundamentals. 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 data scientist is evaluating a custom Einstein model for a lead scoring use case. The model's precision is 0.9, recall is 0.5. What is the most important improvement priority?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Increase recall to reduce false negatives
With a precision of 0.9 and recall of 0.5, the model is highly selective but misses many actual leads (high false negatives). In lead scoring, false negatives mean lost sales opportunities, which is typically more costly than false positives. Therefore, increasing recall to capture more true positives is the most important improvement priority.
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.
✓
Increase recall to reduce false negatives
Why this is correct
Recall is low (0.5), meaning half of actual leads are missed. This should be improved.
Related concept
Read the scenario before looking for a memorised answer.
✗
Increase precision to reduce false positives
Why it's wrong here
Precision is already high (0.9); false positives are relatively low. The main issue is false negatives.
✗
Optimize for an F1 score of 0.7
Why it's wrong here
The current F1 score is about 0.64; aiming for 0.7 is a goal but doesn't indicate primary priority.
✗
Improve overall accuracy above 80%
Why it's wrong here
Accuracy can be misleading if data is imbalanced; recall is the critical issue.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Salesforce often tests the trade-off between precision and recall in imbalanced classification scenarios, where candidates mistakenly focus on improving precision or accuracy without recognizing that low recall (high false negatives) is the critical business problem in lead scoring.
Detailed technical explanation
How to think about this question
Recall (sensitivity) measures the proportion of actual positives correctly identified, calculated as TP/(TP+FN). In lead scoring, false negatives (missed leads) directly impact revenue, so improving recall often involves adjusting the decision threshold or rebalancing the training data. Under the hood, Einstein models use gradient-boosted trees or neural networks, and recall can be tuned by lowering the classification threshold, but this may trade off precision; the optimal threshold depends on the cost of false negatives versus false positives.
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: Increase recall to reduce false negatives — With a precision of 0.9 and recall of 0.5, the model is highly selective but misses many actual leads (high false negatives). In lead scoring, false negatives mean lost sales opportunities, which is typically more costly than false positives. Therefore, increasing recall to capture more true positives is the most important improvement priority.
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.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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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 data scientist is evaluating the performance of an Einstein Discovery model. They observe that the model has high accuracy but low precision for a specific prediction class. What does this indicate?
medium
A.The model is overfitted to the training data.
✓ B.The model correctly predicts most instances but has many false positives for that class.
C.The model correctly predicts most instances but has many false negatives for that class.
D.The model rarely predicts that class, leading to high accuracy.
Why B: High accuracy with low precision for a specific class indicates that while the model correctly classifies the majority of instances overall, it produces a high number of false positives for that class. Precision measures the proportion of positive identifications that were actually correct, so low precision means many of the predicted positive cases are false alarms. In Einstein Discovery, this trade-off is critical when optimizing for business outcomes where false positives are costly.
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Question Discussion
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