- A
Downsample duplicates to reduce their impact but keep all records.
Why wrong: Downsampling reduces but does not eliminate conflicting labels; the model will still learn incorrect patterns.
- B
Use the duplicates as a separate class to indicate noisy data.
Why wrong: Treating duplicates as a separate class does not address the root cause and may distort the prediction task.
- C
Remove all duplicate records and keep only one instance per duplicate group, resolving label conflicts by majority vote.
This cleans the data, removes noise, and provides consistent labels, likely improving model performance.
- D
Keep all duplicates because they represent multiple interactions; increase model complexity to handle them.
Why wrong: Keeping duplicate with conflicting labels adds noise; increasing model complexity may lead to overfitting on noise.
AI Associate Data for AI Practice Question
This AI Associate practice question tests your understanding of data for ai. 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 retail company has implemented a Salesforce AI lead scoring model to prioritize high-value customers. After three months, the model's AUC-ROC score is only 0.55, indicating poor performance. The data scientist reviews the training data and finds that 20% of the records are exact duplicates due to multiple data imports from different sources. The duplicates have inconsistent target labels (some labeled 'converted', others 'not converted'). What should the data scientist do to improve model performance?
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
Remove all duplicate records and keep only one instance per duplicate group, resolving label conflicts by majority vote.
Duplicate records with conflicting labels confuse the model. Removing duplicates and resolving label conflicts (e.g., by majority vote) is the most effective step to clean the data and improve performance.
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.
- ✗
Downsample duplicates to reduce their impact but keep all records.
Why it's wrong here
Downsampling reduces but does not eliminate conflicting labels; the model will still learn incorrect patterns.
- ✗
Use the duplicates as a separate class to indicate noisy data.
Why it's wrong here
Treating duplicates as a separate class does not address the root cause and may distort the prediction task.
- ✓
Remove all duplicate records and keep only one instance per duplicate group, resolving label conflicts by majority vote.
Why this is correct
This cleans the data, removes noise, and provides consistent labels, likely improving model performance.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Keep all duplicates because they represent multiple interactions; increase model complexity to handle them.
Why it's wrong here
Keeping duplicate with conflicting labels adds noise; increasing model complexity may lead to overfitting on noise.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- 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 AI Associate exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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FAQ
Questions learners often ask
What does this AI Associate question test?
Data for AI — This question tests Data for AI — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Remove all duplicate records and keep only one instance per duplicate group, resolving label conflicts by majority vote. — Duplicate records with conflicting labels confuse the model. Removing duplicates and resolving label conflicts (e.g., by majority vote) is the most effective step to clean the data and improve performance.
What should I do if I get this AI Associate question wrong?
Identify which AI Associate exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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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Last reviewed: Jun 23, 2026
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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