Quick Answer
The answer is to analyze the data and model for potential bias, as this is the most ethical first step for an AI bias investigation. When an Einstein Prediction Builder model shows disparate outcomes across demographic groups, the immediate priority is not to disable or retrain the model, but to investigate the root cause of the disparity—whether it stems from biased training data, proxy variables, or skewed feature correlations. On the Salesforce AI Associate exam, this scenario tests your understanding of ethical AI governance, specifically the principle that you must diagnose bias before intervening; a common trap is jumping to remove demographic fields, which fails if other features act as proxies. Remember the mnemonic "Analyze Before Acting" to recall that the first step is always investigation, not reaction.
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.
A Salesforce admin builds an Einstein Prediction Builder model to predict customer churn. The model assigns higher churn risk to customers in a certain demographic group. What is the MOST ethical FIRST step?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "first"
Why it matters: Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
Disable the model immediately
Why wrong: Disabling without investigation may be premature.
Remove demographic features from the model
Why wrong: Bias can persist through correlated features.
Analyze the data and model for potential bias
Investigation is the ethical first step to understand and mitigate bias.
Retrain the model with more data
Why wrong: More data without analysis may not fix bias.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Analyze the data and model for potential bias
Option D is correct because understanding the cause of disparity is essential before taking action. Option A is wrong because disabling the model may lose business value. Option B is wrong as simply retraining may not address root cause. Option C is wrong because removing demographic features might not eliminate bias if correlated.
Key principle: NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Disable the model immediately
Why it's wrong here
Disabling without investigation may be premature.
Remove demographic features from the model
Why it's wrong here
Bias can persist through correlated features.
Analyze the data and model for potential bias
Why this is correct
Investigation is the ethical first step to understand and mitigate bias.
Clue confirmation
The clue word "first" in the question point toward this answer.
Related concept
Static NAT maps one inside address to one outside address.
Retrain the model with more data
Why it's wrong here
More data without analysis may not fix bias.
Common exam traps
NAT is not only about the public address. The inside/outside interface roles and the ACL or rule that matches traffic are just as important.
Detailed technical explanation
NAT questions usually test address translation, overload/PAT behaviour, static mappings and whether the right traffic is being translated. Read the interface direction and address terms carefully.
Key takeaway
NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
Real-world example
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
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related AI Associate NAT questions on configuration and troubleshooting.
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FAQ
Ethical Considerations of AI — This question tests Ethical Considerations of AI — Static NAT maps one inside address to one outside address..
The correct answer is: Analyze the data and model for potential bias — Option D is correct because understanding the cause of disparity is essential before taking action. Option A is wrong because disabling the model may lose business value. Option B is wrong as simply retraining may not address root cause. Option C is wrong because removing demographic features might not eliminate bias if correlated.
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related AI Associate NAT questions on configuration and troubleshooting.
Yes — watch for: "first". Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
Static NAT maps one inside address to one outside address.
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Last reviewed: Jun 23, 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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