Quick Answer
The correct first step is to audit the training data for bias. This is because an AI model for loan approval learns patterns directly from historical data; if that data contains systemic disparities against a specific demographic group, the model will simply replicate those biased patterns as high default risk predictions. Auditing the data allows you to identify skewed labels, imbalanced representation, or proxy variables before any remediation. On the Salesforce AI Associate exam, this question tests your understanding that bias originates in data, not in algorithms—a common trap is assuming you can fix bias by removing demographic features, but correlated features like zip code or income can still encode the same bias. Another trap is thinking retraining with more data helps, but more biased data only amplifies the problem. Remember the memory tip: “Audit first, adjust second”—always check the data source before changing the model or algorithm.
This AI Associate practice question tests your understanding of data for ai. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 data scientist discovers that an AI model used for loan approval predicts high default risk disproportionately for a specific demographic group. What is the first step to address this issue?
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.
Use a different algorithm
Why wrong: Algorithm change does not fix biased data.
Audit the training data for bias
Auditing helps identify and mitigate bias in data.
Remove demographic features from the model
Why wrong: Correlated features may still cause bias.
Retrain the model with more data
Why wrong: More data may still contain 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
Audit the training data for bias
Option B is correct because auditing the training data for bias helps identify if the model learned biased patterns. Option A is wrong because retraining with more data may not solve the bias if the new data also contains bias. Option C is wrong because removing demographic features may not eliminate bias if other correlated features exist. Option D is wrong because changing the algorithm does not address biased data.
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.
Use a different algorithm
Why it's wrong here
Algorithm change does not fix biased data.
Audit the training data for bias
Why this is correct
Auditing helps identify and mitigate bias in data.
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.
Remove demographic features from the model
Why it's wrong here
Correlated features may still cause bias.
Retrain the model with more data
Why it's wrong here
More data may still contain 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
Data for AI — This question tests Data for AI — Static NAT maps one inside address to one outside address..
The correct answer is: Audit the training data for bias — Option B is correct because auditing the training data for bias helps identify if the model learned biased patterns. Option A is wrong because retraining with more data may not solve the bias if the new data also contains bias. Option C is wrong because removing demographic features may not eliminate bias if other correlated features exist. Option D is wrong because changing the algorithm does not address biased data.
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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Same concept, more angles
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. To ensure AI model fairness and avoid biased outcomes, which practice is most critical when preparing training data?
easyWhy C: Option C is correct because using balanced training data across different groups helps prevent bias. Option A is wrong because adding more features can introduce bias. Option B is wrong because increasing model complexity may overfit. Option D is wrong because using only recent data may not represent all demographics.
Last reviewed: Jun 23, 2026
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