- A
Use synthetic data to replace sensitive attributes with random values.
Why wrong: Synthetic data may not preserve real-world correlations and could still leak information.
- B
Include all available attributes to maximize model accuracy, then apply fairness constraints.
Why wrong: Including sensitive attributes risk embedding bias even with fairness constraints; minimisation is safer.
- C
Mask the sensitive attributes but still include them in the model training.
Why wrong: Masking is not enough; if the attribute is used in training, the model can still learn patterns associated with it.
- D
Exclude sensitive attributes from the model features unless they are essential and legally permitted, and ensure no proxies exist.
Data minimisation dictates excluding unnecessary sensitive data; also check for proxies to avoid indirect discrimination.
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.
A financial services company is deploying Einstein Prediction Builder to predict loan default risk. They are concerned about using sensitive attributes like race or gender in the model. Which data governance practice should they apply?
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 sensitive attributes from the model features unless they are essential and legally permitted, and ensure no proxies exist.
Data minimisation is a core principle: only use relevant features for the prediction. Sensitive attributes that could lead to discriminatory decisions should be excluded unless legally required and properly managed.
Key principle: Count usable hosts — not total addresses — and remember that the network and broadcast addresses are not available to hosts in standard IPv4 subnets.
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 synthetic data to replace sensitive attributes with random values.
Why it's wrong here
Synthetic data may not preserve real-world correlations and could still leak information.
- ✗
Include all available attributes to maximize model accuracy, then apply fairness constraints.
Why it's wrong here
Including sensitive attributes risk embedding bias even with fairness constraints; minimisation is safer.
- ✗
Mask the sensitive attributes but still include them in the model training.
Why it's wrong here
Masking is not enough; if the attribute is used in training, the model can still learn patterns associated with it.
- ✓
Exclude sensitive attributes from the model features unless they are essential and legally permitted, and ensure no proxies exist.
Why this is correct
Data minimisation dictates excluding unnecessary sensitive data; also check for proxies to avoid indirect discrimination.
Related concept
CIDR notation defines the prefix length.
Common exam traps
Common exam trap: usable hosts are not the same as total addresses
Subnetting questions often tempt you into counting all addresses. In normal IPv4 subnets, the network and broadcast addresses are not usable host addresses.
Trap categories for this question
Real-world vs exam trap
Synthetic data may not preserve real-world correlations and could still leak information.
Detailed technical explanation
How to think about this question
Subnetting questions test whether you can identify the network, broadcast address, usable range, mask and correct subnet. Slow down enough to calculate the block size correctly.
KKey Concepts to Remember
- CIDR notation defines the prefix length.
- Block size helps identify subnet boundaries.
- Network and broadcast addresses are not usable hosts in normal IPv4 subnets.
- The required host count determines the smallest suitable subnet.
TExam Day Tips
- Write the block size before choosing the subnet.
- Check whether the question asks for hosts, subnets or a specific address range.
- Do not confuse /24, /25, /26 and /27 host counts.
Key takeaway
Count usable hosts — not total addresses — and remember that the network and broadcast addresses are not available to hosts in standard IPv4 subnets.
Real-world example
How this comes up in practice
A security administrator must allow nursing staff to reach a patient records server while blocking access from the guest Wi-Fi VLAN. After applying an extended ACL, traffic is still blocked from nursing workstations. The ACL was applied outbound instead of inbound on the wrong interface. Questions like this test ACL direction and placement rules.
What to study next
Got this wrong? Here's your next step.
Review block sizes, usable host formulas (2^n − 2), and how to find network and broadcast addresses for /24 through /30. Then practise related AI Associate subnetting questions on CIDR, address ranges, and subnet selection.
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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 — CIDR notation defines the prefix length..
What is the correct answer to this question?
The correct answer is: Exclude sensitive attributes from the model features unless they are essential and legally permitted, and ensure no proxies exist. — Data minimisation is a core principle: only use relevant features for the prediction. Sensitive attributes that could lead to discriminatory decisions should be excluded unless legally required and properly managed.
What should I do if I get this AI Associate question wrong?
Review block sizes, usable host formulas (2^n − 2), and how to find network and broadcast addresses for /24 through /30. Then practise related AI Associate subnetting questions on CIDR, address ranges, and subnet selection.
What is the key concept behind this question?
CIDR notation defines the prefix length.
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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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