This AIF-C01 practice question tests your understanding of guidelines for responsible 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 company uses Amazon Comprehend to analyze customer sentiment. They discover the model performs poorly on text with slang from underrepresented groups. What is the most responsible action?
Restrict model use to only standard English
Why wrong: Restricting use excludes users.
Remove slang from input before inference
Why wrong: Removing slang may lose meaning.
Adjust the confidence threshold only for those groups
Why wrong: Group-specific thresholds may be unfair.
Collect more representative training data including slang
Representative data reduces 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
Collect more representative training data including slang
Option D is correct because the core principle of responsible AI requires that models be trained on data that is representative of the populations they serve. Amazon Comprehend's sentiment analysis is a supervised machine learning model; its poor performance on slang from underrepresented groups indicates a training data bias. Collecting more representative training data, including that slang, directly addresses the root cause by enabling the model to learn the linguistic patterns of those groups, improving fairness and accuracy without restricting access or masking the problem.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Restrict model use to only standard English
Why it's wrong here
Restricting use excludes users.
Remove slang from input before inference
Why it's wrong here
Removing slang may lose meaning.
Adjust the confidence threshold only for those groups
Why it's wrong here
Group-specific thresholds may be unfair.
Collect more representative training data including slang
Why this is correct
Representative data reduces bias.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
The trap here is that candidates may choose a quick-fix technical workaround (like removing slang or adjusting thresholds) instead of recognizing that the responsible AI approach requires addressing the root cause of bias through data representativeness, which is a core ethical and technical principle tested in the AIF-C01 exam.
Detailed technical explanation
Under the hood, Amazon Comprehend uses a deep learning model (e.g., a transformer-based architecture) trained on a large corpus of text. The model's embeddings and attention mechanisms learn to associate specific word patterns with sentiment labels. If the training data lacks sufficient examples of slang from certain groups, the model will have high loss on those inputs, leading to poor generalization. In a real-world scenario, a customer service chatbot using Comprehend might misinterpret a positive slang phrase like 'that's lit' as negative, causing incorrect routing; retraining with diverse data reduces this error by adjusting the model's weights to correctly map those phrases.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A company's IT admin needs to give a contractor read-only access to production logs without sharing account credentials. Using role-based access control (RBAC) and temporary scoped permissions — not a permanent shared password — is the correct pattern. Questions like this test whether you can apply least-privilege access across cloud identity services.
What to study next
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FAQ
Guidelines for Responsible AI — This question tests Guidelines for Responsible AI — Read the scenario before looking for a memorised answer..
The correct answer is: Collect more representative training data including slang — Option D is correct because the core principle of responsible AI requires that models be trained on data that is representative of the populations they serve. Amazon Comprehend's sentiment analysis is a supervised machine learning model; its poor performance on slang from underrepresented groups indicates a training data bias. Collecting more representative training data, including that slang, directly addresses the root cause by enabling the model to learn the linguistic patterns of those groups, improving fairness and accuracy without restricting access or masking the problem.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 25, 2026
This AIF-C01 practice question is part of Courseiva's free Amazon Web Services 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 AIF-C01 exam.
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