AI Associate Ethical Considerations of AI Practice Question
This AI Associate practice question tests your understanding of ethical considerations of ai. 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.
Which THREE factors should an AI Associate consider when evaluating a model for potential bias?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Whether features are correlated with protected attributes.
Option B is correct because if a feature is correlated with a protected attribute (e.g., race, gender, age), the model may inadvertently learn and perpetuate discriminatory patterns, even if the protected attribute itself is not used as an input. This is a key source of indirect or proxy bias in machine learning systems.
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.
✗
The complexity of the model architecture.
Why it's wrong here
Complexity does not indicate bias.
✓
Whether features are correlated with protected attributes.
Why this is correct
Correlation can lead to proxy discrimination.
Related concept
Read the scenario before looking for a memorised answer.
✓
Disparities in model performance metrics across groups.
Why this is correct
Performance disparities signal bias.
Related concept
Read the scenario before looking for a memorised answer.
✗
The date the model was last deployed.
Why it's wrong here
Deployment date is not a bias factor.
✓
Whether the training data is representative of all groups.
Why this is correct
Non-representative data can cause bias.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Salesforce often tests the misconception that model complexity or deployment recency are relevant to bias detection, when in fact bias is rooted in data representation and feature correlations with protected attributes.
Detailed technical explanation
How to think about this question
Under the hood, bias evaluation often involves computing correlation coefficients (e.g., Pearson or Spearman) between features and protected attributes, or using fairness metrics like demographic parity (P(Ŷ=1|A=a) = P(Ŷ=1|A=b)) and equalized odds. In a real-world scenario, a credit scoring model using 'zip code' may be correlated with race, leading to disparate impact even if race is excluded from training data.
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 small business has 20 workstations on the 192.168.1.0/24 network and one public IP from its ISP. The router uses PAT (NAT overload) so all 20 devices share one public address using different source ports. NAT questions test whether you understand the four address terms and which direction each translation applies.
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.
Ethical Considerations of AI — This question tests Ethical Considerations of AI — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Whether features are correlated with protected attributes. — Option B is correct because if a feature is correlated with a protected attribute (e.g., race, gender, age), the model may inadvertently learn and perpetuate discriminatory patterns, even if the protected attribute itself is not used as an input. This is a key source of indirect or proxy bias in machine learning systems.
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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Question Discussion
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