AI Associate Data for AI Practice Question
Exhibit
{
"maskingPolicy": {
"name": "MaskSSN",
"fields": ["SSN", "CreditCard"],
"maskingType": "partial",
"character": "*",
"showLastFour": true
}
}Refer to the exhibit. What effect does this masking policy have on the data used for training an Einstein model?
⚠ Common exam trap
Salesforce often tests the distinction between masking, encryption, and removal, where candidates mistakenly think masking is equivalent to encryption or complete deletion, but masking specifically preserves partial data for model training while hiding sensitive details.
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
✓
SSN and CreditCard fields are partially masked, showing only the last four characters.
The masking policy in Einstein applies a partial mask to sensitive fields like SSN and CreditCard, showing only the last four characters while obscuring the rest. This ensures that the data used for training retains its structural utility for model learning without exposing full sensitive values, which is why option D is correct.
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 SSN is masked.
Why it's wrong here
Both fields are listed in the policy.
- ✗
SSN and CreditCard fields are encrypted.
Why it's wrong here
Masking is different from encryption.
- ✗
SSN and CreditCard fields are completely removed from training data.
Why it's wrong here
The policy masks, not removes.
- ✓
SSN and CreditCard fields are partially masked, showing only the last four characters.
Why this is correct
Explicitly defined by showLastFour and maskingType partial.
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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.