- A
Analyze the training data to determine if there is sampling bias or labeling bias that caused the model to associate minority ownership with higher risk.
Bias often stems from training data; analyzing data for imbalances or incorrect labels is the first logical step.
- B
Apply a disparate impact analysis to quantify the adverse impact and then adjust the decision threshold.
Why wrong: While useful, quantifying impact should come after understanding the data source of bias.
- C
Examine the model's weights and activations to identify which features contribute to the disparity.
Why wrong: Deep neural network weights are not easily interpretable; this approach is impractical for understanding bias.
- D
Retrain the model with a fairness constraint that penalizes disparities in approval rates.
Why wrong: Retraining without understanding the root cause may not address the underlying bias and could harm accuracy.
AI Associate Ethical Considerations of AI Practice Question
This AI Associate practice question tests your understanding of ethical considerations of 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 financial services company deploys an AI system to approve small business loans. The system uses a deep neural network trained on historical loan data. After deployment, an internal audit reveals that the approval rate for minority-owned businesses is 15% lower than for non-minority-owned businesses with similar financial profiles. The company's AI Ethics policy requires that AI systems be fair and transparent. The data science team has access to the training data, model architecture, and feature importance scores. The company wants to understand why the disparity exists and take corrective action. Which approach should the team take 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 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
Analyze the training data to determine if there is sampling bias or labeling bias that caused the model to associate minority ownership with higher risk.
Option A is correct because the first step in diagnosing an AI fairness issue is to audit the training data for biases such as sampling bias (e.g., underrepresentation of minority-owned businesses) or labeling bias (e.g., historical loan officers unfairly labeling minority applicants as higher risk). Since the team has access to the training data, analyzing it directly addresses the root cause of the disparity before making model-level changes. This aligns with the AI Ethics policy requirement for transparency, as data bias is a common source of unfair outcomes in deep neural networks trained on historical data.
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.
- ✓
Analyze the training data to determine if there is sampling bias or labeling bias that caused the model to associate minority ownership with higher risk.
Why this is correct
Bias often stems from training data; analyzing data for imbalances or incorrect labels is the first logical step.
Clue confirmation
The clue word "first" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Apply a disparate impact analysis to quantify the adverse impact and then adjust the decision threshold.
Why it's wrong here
While useful, quantifying impact should come after understanding the data source of bias.
- ✗
Examine the model's weights and activations to identify which features contribute to the disparity.
Why it's wrong here
Deep neural network weights are not easily interpretable; this approach is impractical for understanding bias.
- ✗
Retrain the model with a fairness constraint that penalizes disparities in approval rates.
Why it's wrong here
Retraining without understanding the root cause may not address the underlying bias and could harm accuracy.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Salesforce often tests the principle that data bias is the most common root cause of AI fairness issues, tempting candidates to jump to model-level fixes (like threshold adjustment or fairness constraints) instead of first auditing the training data for sampling or labeling bias.
Detailed technical explanation
How to think about this question
Under the hood, sampling bias in training data can cause a deep neural network to learn spurious correlations, such as associating minority ownership with higher default risk due to historical lending discrimination. Feature importance scores from a model like a gradient-boosted tree might show 'minority ownership' as a high-importance feature, but this could reflect biased labels rather than true risk. In practice, a thorough data audit might involve stratified analysis of approval rates across demographic groups, checking for label noise, and using techniques like reweighting or synthetic data generation to correct imbalances before retraining.
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 practitioner preparing for the AI Associate exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
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.
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Ethical Considerations of AI — study guide chapter
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FAQ
Questions learners often ask
What does this AI Associate question test?
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: Analyze the training data to determine if there is sampling bias or labeling bias that caused the model to associate minority ownership with higher risk. — Option A is correct because the first step in diagnosing an AI fairness issue is to audit the training data for biases such as sampling bias (e.g., underrepresentation of minority-owned businesses) or labeling bias (e.g., historical loan officers unfairly labeling minority applicants as higher risk). Since the team has access to the training data, analyzing it directly addresses the root cause of the disparity before making model-level changes. This aligns with the AI Ethics policy requirement for transparency, as data bias is a common source of unfair outcomes in deep neural networks trained on historical data.
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 →
Last reviewed: Jun 30, 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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