AIF-C01 Guidelines for Responsible AI Practice Question
A team is using Amazon Comprehend to analyze customer feedback for sentiment. They want to detect and mitigate potential bias against certain demographic groups. Which TWO approaches should they consider? (Choose TWO.)
⚠ Common exam trap
Many candidates confuse AWS WAF or CloudTrail as general-purpose bias detection tools, when in fact they serve entirely different security and auditing functions, while the correct approaches require specialized ML fairness services like SageMaker Clarify and balanced training data practices.
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
✓
Use SageMaker Clarify to compute bias metrics on the training data.
Option D is correct because SageMaker Clarify is the AWS service purpose-built to detect bias in datasets and models, computing metrics such as class imbalance (CI) and difference in proportions of labels (DPL) across demographic groups, which directly addresses measuring bias in the training data used for sentiment analysis. Option E is correct because Amazon Comprehend custom classification lets you train a custom model on your own labeled data, and ensuring that training data is balanced across demographic groups reduces the risk that the classifier learns and amplifies skewed associations, thereby mitigating bias in the resulting sentiment predictions. Option A is not appropriate because AWS WAF is a web application firewall that filters HTTP/S traffic against exploits like SQL injection and XSS, not a tool for detecting or mitigating bias in text sentiment. Option B is not appropriate because AWS CloudTrail only records API activity for auditing and governance, and does not analyze or reduce bias. Option C is not appropriate because Amazon Rekognition performs image and video analysis (such as facial detection and moderation), which is unrelated to detecting bias in textual customer feedback sentiment.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use AWS WAF to filter out biased comments.
Why it's wrong here
AWS WAF filters HTTP traffic against web exploits; it cannot inspect model outputs for demographic bias. It tempts because filtering unwanted content sounds like bias mitigation, but the requirement is detecting skewed sentiment across groups. That needs bias analysis on the data and outputs, such as Amazon Comprehend's own fairness evaluation.
- ✗
Use AWS CloudTrail to audit API calls.
Why it's wrong here
CloudTrail records API activity for auditing and security, not model bias. It tempts because auditing sounds like governance of fairness, but it captures who called what, never whether sentiment scores differ across demographic groups. Detecting that skew requires examining the training data and prediction outputs directly.
- ✗
Use Amazon Rekognition to verify images.
Why it's wrong here
Rekognition analyses images and video; the feedback here is text sentiment, so it cannot detect demographic bias in Comprehend's outputs. It tempts because it is another AI service with fairness features, but the modality is wrong. Bias detection must operate on the text data and model predictions.
- ✓
Use SageMaker Clarify to compute bias metrics on the training data.
Why this is correct
SageMaker Clarify computes bias metrics such as class imbalance and disparate impact across demographic groups in training data, exposing skew before it propagates into sentiment predictions. That satisfies the requirement to detect bias against specific groups systematically.
- ✓
Use Comprehend custom classification with balanced training data across groups.
Why this is correct
Balancing training examples across demographic groups prevents the custom classifier from learning sentiment patterns dominated by one group, reducing disparate impact in predictions. Comprehend custom classification trains on supplied labelled data, so group-balanced sampling directly mitigates representational bias.
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Same concept, more angles
1 more way this is tested on AIF-C01
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. 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?
medium- A.Restrict model use to only standard English
- B.Remove slang from input before inference
- C.Adjust the confidence threshold only for those groups
- ✓ D.Collect more representative training data including slang
Why D: 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.
JA
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.