- A
Enabling zero data retention in the Trust Layer
Why wrong: Zero data retention prevents customer data from being used to train base models, but does not prevent PII from being used in custom models.
- B
Data anonymization via the Einstein Trust Layer
Why wrong: The Trust Layer masks PII in prompts and responses but does not automatically exclude PII from training datasets.
- C
Regularly auditing the model for bias
Why wrong: Auditing for bias checks fairness, not the presence of PII in training data.
- D
Data minimisation by selecting only non-PII fields as predictors
Deliberately excluding PII fields from the prediction definition is the best way to ensure PII is not used in training.
Question 320 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.
An organization using Einstein Prediction Builder wants to ensure that no customer personally identifiable information (PII) is used in model training. Which data governance practice should they enforce?
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 by selecting only non-PII fields as predictors
Option D is correct because the question specifically asks how to ensure no PII is used in model training. Data minimization by selecting only non-PII fields as predictors directly prevents PII from entering the training dataset at the source. This is a proactive governance practice that avoids reliance on post-processing or masking, which may still expose PII during intermediate steps.
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.
- ✗
Enabling zero data retention in the Trust Layer
Why it's wrong here
Zero data retention prevents customer data from being used to train base models, but does not prevent PII from being used in custom models.
- ✗
Data anonymization via the Einstein Trust Layer
Why it's wrong here
The Trust Layer masks PII in prompts and responses but does not automatically exclude PII from training datasets.
- ✗
Regularly auditing the model for bias
Why it's wrong here
Auditing for bias checks fairness, not the presence of PII in training data.
- ✓
Data minimisation by selecting only non-PII fields as predictors
Why this is correct
Deliberately excluding PII fields from the prediction definition is the best way to ensure PII is not used in training.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse the Einstein Trust Layer's runtime masking with training-time data governance, assuming anonymization prevents PII from being used in model training when it only masks data during prediction.
Detailed technical explanation
How to think about this question
Under the hood, Einstein Prediction Builder uses the Einstein Trust Layer to mask sensitive fields only at runtime for prediction requests, not during model training. If PII fields are included as predictors, the training pipeline will process the raw data before any masking is applied. Data minimization ensures that the training dataset never contains PII columns, which is the only guaranteed way to prevent PII from influencing model weights. In a real-world scenario, a healthcare organization might exclude patient IDs and SSNs from the predictor list to comply with HIPAA, even if the Trust Layer is enabled.
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 network engineer segments a warehouse floor into three subnets: 20 scanners, 5 printers, and 2 management hosts. Picking the wrong mask wastes addresses or leaves too few usable hosts. Exam questions test whether you can apply CIDR notation, calculate block size, and identify the correct usable-host range for a given prefix.
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: Data minimisation by selecting only non-PII fields as predictors — Option D is correct because the question specifically asks how to ensure no PII is used in model training. Data minimization by selecting only non-PII fields as predictors directly prevents PII from entering the training dataset at the source. This is a proactive governance practice that avoids reliance on post-processing or masking, which may still expose PII during intermediate steps.
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.
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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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