The correct first step is to re-evaluate the training data to ensure balanced representation and consider re-sampling techniques. This is because accuracy disparities for underrepresented groups typically stem from a skewed class distribution in the historical data, where the model learns patterns from the majority group while failing to generalize to minority populations. By auditing the data and applying re-sampling methods like oversampling the minority class or undersampling the majority, you directly address the root cause of the bias before altering the model architecture or features. On the Salesforce AI Associate exam, this scenario tests your understanding of the data-centric approach to fairness, often appearing as a trap where candidates mistakenly jump to feature engineering or algorithm changes. A common memory tip is “data first, model second”—always audit the training data for representational imbalance as the initial diagnostic step when you encounter lower accuracy for underrepresented groups.
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.
A healthcare organization is deploying an AI model to predict patient readmission risk. The model was trained on historical data that underrepresented minority populations. During testing, the model shows lower accuracy for those groups. What should the data scientist do first?
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.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Re-evaluate the training data to ensure balanced representation and consider re-sampling techniques.
Option D is correct because the first step in addressing model bias is to audit the training data for representational imbalance. Re-evaluating the data and applying re-sampling techniques (e.g., oversampling minority groups or undersampling the majority) directly targets the root cause of the disparity—skewed class distributions—before modifying the model or its features.
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.
✗
Remove sensitive attributes like race and gender from the training data.
Why it's wrong here
Removing attributes does not eliminate bias from correlated features.
✗
Ignore the disparity because the model's overall accuracy is acceptable.
Why it's wrong here
Fairness requires equitable performance across groups.
✗
Retrain the model with more complex algorithms to improve accuracy.
Why it's wrong here
Complex algorithms may amplify bias if data issues persist.
✓
Re-evaluate the training data to ensure balanced representation and consider re-sampling techniques.
Why this is correct
Ensuring data representativeness addresses root cause of bias.
Clue confirmation
The clue word "first" in the question point toward this answer.
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 removing sensitive attributes or improving model complexity automatically fixes bias, when in fact the data imbalance must be addressed first at the dataset level.
Detailed technical explanation
How to think about this question
Under the hood, re-sampling techniques like SMOTE (Synthetic Minority Over-sampling Technique) generate synthetic samples for underrepresented classes, while cost-sensitive learning adjusts the loss function to penalize misclassifications of minority groups more heavily. In healthcare, a model with biased training data might show high overall AUC but poor calibration for minority populations, leading to systematic under-prediction of readmission risk and unequal care allocation.
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: Re-evaluate the training data to ensure balanced representation and consider re-sampling techniques. — Option D is correct because the first step in addressing model bias is to audit the training data for representational imbalance. Re-evaluating the data and applying re-sampling techniques (e.g., oversampling minority groups or undersampling the majority) directly targets the root cause of the disparity—skewed class distributions—before modifying the model or its features.
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.
Are there clue words in this question I should notice?
Yes — watch for: "first". Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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 →
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. During model development, the data scientist realizes the training data is not representative of the intended population. What should they do?
medium
A.Remove the underrepresented groups from the scope.
B.Increase model regularization.
C.Use the data as is, as the model will generalize.
✓ D.Augment data with synthetic samples for underrepresented groups.
Why D: Option B is correct because augmenting with synthetic data for underrepresented groups helps create a more representative dataset. Option A is wrong because using non-representative data can lead to biased models. Option C is wrong because removing groups from scope can lead to exclusion. Option D is wrong because regularization does not address representativeness.
Last reviewed: Jun 30, 2026
Question Discussion
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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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