- A
Speech recognition
Why wrong: Speech recognition would be needed for audio feedback, but the scenario focuses on text.
- B
Sentiment analysis
Sentiment analysis measures emotional tone, key for understanding customer feelings.
- C
Image recognition
Why wrong: Image recognition is not directly applicable to text-based feedback.
- D
Text classification
Text classification organizes feedback into categories like product, service, or pricing.
- E
Predictive lead scoring
Why wrong: Lead scoring is sales-focused, not for analyzing existing customer feedback.
Question 168 of 1,000
AI Capabilities for Customer Feedback — Sentiment Analysis and Text Classification
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 company wants to use AI to analyze customer feedback from surveys and social media. Which TWO capabilities are most relevant?
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
✓
Sentiment analysis
Sentiment analysis detects overall sentiment (positive/negative), and text classification can categorize feedback by topic. Entity extraction might be useful but is less central for broad feedback analysis.
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.
- ✗
Speech recognition
Why it's wrong here
Speech recognition would be needed for audio feedback, but the scenario focuses on text.
- ✓
Sentiment analysis
Why this is correct
Sentiment analysis measures emotional tone, key for understanding customer feelings.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Image recognition
Why it's wrong here
Image recognition is not directly applicable to text-based feedback.
- ✓
Text classification
Why this is correct
Text classification organizes feedback into categories like product, service, or pricing.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Predictive lead scoring
Why it's wrong here
Lead scoring is sales-focused, not for analyzing existing customer feedback.
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.
Trap categories for this question
Scenario analysis trap
Speech recognition would be needed for audio feedback, but the scenario focuses on text.
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?
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: Sentiment analysis — Sentiment analysis detects overall sentiment (positive/negative), and text classification can categorize feedback by topic. Entity extraction might be useful but is less central for broad feedback analysis.
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
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 →
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Last reviewed: Jul 4, 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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