The answer is to retrain the model using a more diverse dataset that represents the target population. This is the most effective approach because imbalanced training data directly causes the model to learn biased patterns, so fixing the bias requires addressing the root cause by retraining with diverse data that reflects the full demographic range of the intended deployment environment. On the Salesforce AI Associate exam, this scenario tests your understanding of ethical AI principles and the importance of representative data collection—a common trap is choosing to simply monitor the model or adjust decision thresholds, which only manage symptoms without correcting the underlying bias. A useful memory tip is to think of the “GIGO” principle: Garbage In, Garbage Out—if your training data lacks diversity, your model’s outputs will be skewed, so you must fix the input to fix the output.
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 hospital uses an AI model to predict patient deterioration. The model was trained on data from a single hospital with a predominantly white patient population. When deployed at a hospital serving a diverse population, the model underperforms for minority groups. What is the most effective way to address this ethical issue?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Retrain the model using a more diverse dataset that represents the target population.
Option D is correct because retraining with diverse data from the target population addresses the root cause. Option A is wrong as it only monitors without improvement. Option B is wrong because adjusting thresholds may not fix the underlying model bias. Option C is wrong because creating separate models for each group could be logistically complex and stigmatizing.
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
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
Create separate models for each demographic group to ensure accuracy.
Why it's wrong here
This may lead to unequal treatment and is not efficient.
✗
Continuously monitor model performance across demographic groups and report disparities.
Why it's wrong here
Monitoring is necessary but not sufficient.
✓
Retrain the model using a more diverse dataset that represents the target population.
Why this is correct
Diverse training data improves fairness and performance.
Related concept
Static NAT maps one inside address to one outside address.
✗
Adjust the decision threshold for minority groups to improve sensitivity.
Why it's wrong here
Threshold adjustment does not correct the model's predictive bias.
Common exam traps
Common exam trap: NAT rules depend on direction and matching traffic
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
How to think about this question
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.
KKey Concepts to Remember
Static NAT maps one inside address to one outside address.
PAT allows many inside hosts to share one public address using ports.
Inside local and inside global describe the private and translated addresses.
NAT ACLs identify traffic for translation, not always security filtering.
TExam Day Tips
→Identify inside and outside interfaces first.
→Check whether the scenario needs static NAT, dynamic NAT or PAT.
→Do not confuse NAT matching ACLs with normal packet-filtering intent.
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
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.
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.
Ethical Considerations of AI — This question tests Ethical Considerations of AI — Static NAT maps one inside address to one outside address..
What is the correct answer to this question?
The correct answer is: Retrain the model using a more diverse dataset that represents the target population. — Option D is correct because retraining with diverse data from the target population addresses the root cause. Option A is wrong as it only monitors without improvement. Option B is wrong because adjusting thresholds may not fix the underlying model bias. Option C is wrong because creating separate models for each group could be logistically complex and stigmatizing.
What should I do if I get this AI Associate question wrong?
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.
What is the key concept behind this question?
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 →
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. An AI model for predicting employee performance is found to have a higher false positive rate for women than for men. What is the best course of action?
medium
✓ A.Investigate the cause and retrain the model to reduce bias
B.Lower the decision threshold for women to equalize false positive rates
C.Proceed with deployment because the overall accuracy is acceptable
D.Use the model but require manual review for all female candidates
Why A: Option A is correct because a higher false positive rate for women indicates the model has learned biased patterns from the training data, likely due to imbalanced or skewed historical data. Investigating the cause—such as examining feature correlations, data distribution, and model architecture—allows for targeted retraining (e.g., reweighting, adversarial debiasing, or fairness constraints) to reduce bias without sacrificing overall performance. This aligns with ethical AI principles and regulatory expectations, ensuring the model is fair across demographic groups.
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.