AI Associate Ethical AI and Data Privacy Practice Question
A company wants to use Einstein Prediction Builder to predict which customers are likely to churn. They have a dataset that includes customers' names, email addresses, and detailed purchase history. According to the data minimisation principle, which fields should be included in the model?
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
✓
Only the purchase history
Data minimisation means using only the data necessary for the prediction. Purchase history is relevant to churn prediction, while names and email addresses are personally identifiable information (PII) that are not needed for the model and should be excluded to minimize privacy risks.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Only the purchase history
Why this is correct
Purchase history is relevant; names and emails are unnecessary PII. Excluding them minimizes data use and privacy risk.
- ✗
Only the email addresses, since they are unique identifiers
Why it's wrong here
Email addresses are PII and not predictive of churn; they should not be used.
- ✗
All three fields because more data improves accuracy
Why it's wrong here
Including PII unnecessarily violates data minimisation and may increase compliance burden without improving prediction.
- ✗
Names and purchase history, but not email addresses
Why it's wrong here
Names are still PII and not needed; purchase history alone is sufficient.
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Written by Johnson Ajibi, MSc IT Security
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