AI Associate Ethical AI and Data Privacy Practice Question
A data scientist is training a custom AI model using Salesforce Data. They want to ensure that the model does not inadvertently learn patterns from Personal Identifiable Information (PII) fields such as Social Security numbers. Which approach aligns with Salesforce's responsible AI practices?
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
✓
Exclude PII fields from the training dataset entirely and use only non-sensitive features.
Data minimisation and privacy by design dictate that PII should be excluded from training data to prevent the model from memorizing or relying on sensitive information.
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 the PII fields but restrict the model from outputting them through post-processing filters.
Why it's wrong here
Post-processing does not prevent the model from internalizing PII patterns.
- ✓
Exclude PII fields from the training dataset entirely and use only non-sensitive features.
Why this is correct
Excluding PII prevents the model from accessing sensitive data, aligning with data minimisation.
- ✗
Anonymize the PII fields by hashing them and include the hashed values as features.
Why it's wrong here
Hashing may not prevent re-identification and the model could still learn correlations.
- ✗
Include all fields initially, then use feature importance to prune PII fields after training.
Why it's wrong here
The model could still learn from PII during training; pruning after does not remove embedded information.
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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.