AI Associate Ethical AI and Data Privacy Practice Question
A healthcare organization uses Einstein Prediction Builder to predict patient no-show rates. They want to ensure that protected health information (PHI) like patient names and social security numbers are not used in the model. Which Salesforce Trusted AI principle or feature directly addresses this requirement?
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
✓
Data minimisation
Data minimisation is a key principle: only use relevant features for the model and avoid including sensitive PII unnecessarily. The Einstein Trust Layer's PII masking also helps, but the principle that directly addresses using only relevant fields is data minimisation.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Zero data retention
Why it's wrong here
Zero data retention ensures customer data is not stored after prediction, but does not prevent it from being used as a feature during prediction.
- ✗
Explainability
Why it's wrong here
Explainability ensures the model's outputs are understandable, but does not directly restrict which data fields are used.
- ✗
Human oversight
Why it's wrong here
Human oversight requires review of AI actions, but does not prevent PHI from being used as a feature.
- ✓
Data minimisation
Why this is correct
Data minimisation means using only the data necessary for the task, avoiding sensitive PII in model training.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.