- A
Use a single expert labeler for all data.
Why wrong: Single labeler may have personal bias and doesn't allow for cross-validation.
- B
Use majority voting among multiple labelers.
Majority voting aggregates judgments, improving accuracy and consistency.
- C
Label all data by a single expert labeler.
Why wrong: Same as B; no redundancy.
- D
Allow each labeler to interpret guidelines freely.
Why wrong: Lack of standardized guidelines leads to inconsistent labels.
Question 803 of 1,000
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 team is labeling text data for a sentiment analysis model. To ensure consistency and quality, which practice should they prioritize?
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
✓
Use majority voting among multiple labelers.
Majority voting among multiple labelers reduces individual bias and errors, improving label consistency and quality for training data. This approach is standard in supervised learning for sentiment analysis because it aggregates diverse judgments, leading to more reliable ground truth labels.
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.
- ✗
Use a single expert labeler for all data.
Why it's wrong here
Single labeler may have personal bias and doesn't allow for cross-validation.
- ✓
Use majority voting among multiple labelers.
Why this is correct
Majority voting aggregates judgments, improving accuracy and consistency.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Label all data by a single expert labeler.
Why it's wrong here
Same as B; no redundancy.
- ✗
Allow each labeler to interpret guidelines freely.
Why it's wrong here
Lack of standardized guidelines leads to inconsistent labels.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Salesforce often tests the misconception that a single expert labeler guarantees higher quality, when in fact multiple labelers with majority voting reduce bias and improve reliability for training data.
Detailed technical explanation
How to think about this question
Majority voting is a form of ensemble labeling that leverages the wisdom of the crowd to mitigate individual annotator noise. In practice, inter-annotator agreement metrics like Cohen's kappa or Fleiss' kappa are used to measure consistency, and disagreements are often resolved through adjudication or probabilistic label aggregation. For sentiment analysis, this is critical because subjective labels (e.g., 'positive' vs. 'neutral') can vary widely, and majority voting provides a simple yet effective way to converge on a consensus label.
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.
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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: Use majority voting among multiple labelers. — Majority voting among multiple labelers reduces individual bias and errors, improving label consistency and quality for training data. This approach is standard in supervised learning for sentiment analysis because it aggregates diverse judgments, leading to more reliable ground truth labels.
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.
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Last reviewed: Jun 30, 2026
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