- A
Include all available fields to maximize model accuracy
Why wrong: Including unnecessary fields violates data minimization.
- B
Use only fields directly relevant to purchase history and engagement, excluding PII like social security numbers
Using only relevant fields reduces privacy risk and aligns with minimization.
- C
Obtain explicit consent for every field used
Why wrong: Consent is required, but minimization is separate; consent does not justify using irrelevant fields.
- D
Anonymize all data before training, including purchase amounts
Why wrong: Anonymization is good, but purchase amounts may be necessary; excluding them could harm accuracy unnecessarily.
Question 690 of 1,000
AI Associate Ethical AI and Data Privacy Practice Question
This AI Associate practice question tests your understanding of ethical ai and data privacy. 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 concerned about the data minimization principle when using AI to predict customer lifetime value. Which approach aligns with this principle?
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 only fields directly relevant to purchase history and engagement, excluding PII like social security numbers
Option B aligns with the data minimization principle by restricting data collection to only fields directly relevant to the prediction task (purchase history and engagement) and explicitly excluding personally identifiable information (PII) like social security numbers. This reduces privacy risk and complies with regulations such as GDPR and CCPA, which require that data collected be adequate, relevant, and limited to what is necessary for the processing purpose.
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.
- ✗
Include all available fields to maximize model accuracy
Why it's wrong here
Including unnecessary fields violates data minimization.
- ✓
Use only fields directly relevant to purchase history and engagement, excluding PII like social security numbers
Why this is correct
Using only relevant fields reduces privacy risk and aligns with minimization.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Obtain explicit consent for every field used
Why it's wrong here
Consent is required, but minimization is separate; consent does not justify using irrelevant fields.
- ✗
Anonymize all data before training, including purchase amounts
Why it's wrong here
Anonymization is good, but purchase amounts may be necessary; excluding them could harm accuracy unnecessarily.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that obtaining consent or anonymizing data automatically satisfies data minimization, when in fact these measures address different principles (consent and data security) and do not reduce the scope of data collected.
Detailed technical explanation
How to think about this question
Data minimization is a core principle under Article 5(1)(c) of the GDPR, requiring that personal data be 'adequate, relevant and limited to what is necessary' for the processing purpose. In practice, this means performing a data protection impact assessment (DPIA) to identify the minimum set of features needed for the model, then implementing technical controls like feature selection and data masking to exclude extraneous fields. For example, a customer lifetime value model might only need transaction timestamps, product categories, and purchase amounts—not full names or social security numbers—to make accurate predictions.
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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Ethical AI and Data Privacy — study guide chapter
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FAQ
Questions learners often ask
What does this AI Associate question test?
Ethical AI and Data Privacy — This question tests Ethical AI and Data Privacy — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use only fields directly relevant to purchase history and engagement, excluding PII like social security numbers — Option B aligns with the data minimization principle by restricting data collection to only fields directly relevant to the prediction task (purchase history and engagement) and explicitly excluding personally identifiable information (PII) like social security numbers. This reduces privacy risk and complies with regulations such as GDPR and CCPA, which require that data collected be adequate, relevant, and limited to what is necessary for the processing purpose.
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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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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