- A
Use only data from the last month.
Why wrong: Using only recent data may miss long-term patterns and reduce sample size, harming model robustness.
- B
Ensure data is representative of all customer demographics.
Representative data prevents model bias and improves generalization across customer segments.
- C
Remove all records with missing values.
Why wrong: Removing all missing values may discard valuable data; imputation or handling missingness is preferred.
- D
Label data manually by a single annotator.
Why wrong: Single-annotator labeling introduces individual bias; best practice is multiple annotators with consensus.
- E
Anonymize personally identifiable information (PII) before training.
Anonymization protects customer privacy and complies with regulations like GDPR.
Quick Answer
The answer is to anonymize personally identifiable information (PII) before training and ensure the dataset is representative of the target population. Anonymizing PII is a critical data preparation best practice for AI sentiment analysis because it protects customer privacy and prevents the model from learning biased correlations tied to specific individuals, which aligns with ethical AI principles. Ensuring representative data, meanwhile, means the training set reflects the full diversity of customer demographics, language, and sentiment expressions, which directly reduces model bias and improves generalization. On the Salesforce AI Associate exam, this question tests your understanding of the data preparation phase in the AI lifecycle, often appearing as a two-correct-answer scenario where distractors like “remove all records with missing values” or “use only recent data” are common traps. A useful memory tip is to think of the acronym PAR: Protect PII, Avoid bias with Representative data, and never Remove missing values outright.
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 company is preparing customer data to train a custom AI model for sentiment analysis. Which two data preparation best practices should they follow? (Choose two.)
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"best"Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
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
Ensure data is representative of all customer demographics.
Ensuring representative data and anonymizing PII are critical for model fairness and privacy. Removing all records with missing values can discard useful information; using only recent data may introduce bias; single-annotator labeling can cause subjective bias.
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 only data from the last month.
Why it's wrong here
Using only recent data may miss long-term patterns and reduce sample size, harming model robustness.
- ✓
Ensure data is representative of all customer demographics.
Why this is correct
Representative data prevents model bias and improves generalization across customer segments.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Remove all records with missing values.
Why it's wrong here
Removing all missing values may discard valuable data; imputation or handling missingness is preferred.
- ✗
Label data manually by a single annotator.
Why it's wrong here
Single-annotator labeling introduces individual bias; best practice is multiple annotators with consensus.
- ✓
Anonymize personally identifiable information (PII) before training.
Why this is correct
Anonymization protects customer privacy and complies with regulations like GDPR.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
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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Learn the concepts, then practise the questions
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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: Ensure data is representative of all customer demographics. — Ensuring representative data and anonymizing PII are critical for model fairness and privacy. Removing all records with missing values can discard useful information; using only recent data may introduce bias; single-annotator labeling can cause subjective bias.
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.
Are there clue words in this question I should notice?
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
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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