AIF-C01 Guidelines for Responsible AI Practice Question
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?
⚠ Common exam trap
Watch out — candidates often 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.
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
✓
Collect more representative training data including slang
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.
Answer analysis
Option-by-option breakdown
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 to standard English abandons the affected customers rather than addressing the bias, so sentiment for slang speakers goes unmeasured. It is tempting because constraining input scope does raise measured accuracy, and that approach fits a system where non-standard text is genuinely out of scope.
- ✗
Remove slang from input before inference
Why it's wrong here
Stripping slang discards the very tokens carrying sentiment, so those customers' opinions are silently excluded from analysis. It is tempting because input normalisation is standard preprocessing, and it would be correct where noise or formatting artefacts, rather than dialect, degrade inference quality.
- ✗
Adjust the confidence threshold only for those groups
Why it's wrong here
Per-group thresholds mask the disparity by changing how predictions are counted, leaving the underlying model bias intact. It is tempting because threshold tuning is a legitimate calibration technique, and it would be correct where class imbalance, not representation, drives errors across all groups equally.
- ✓
Collect more representative training data including slang
Why this is correct
Collecting representative training data that includes slang from underrepresented groups addresses the root cause: the model's vocabulary and patterns were learned from unrepresentative text. This improves sentiment accuracy for those groups rather than masking the disparity.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.