AIF-C01 Guidelines for Responsible AI Practice Question
A media company uses Amazon Rekognition to automatically moderate user-uploaded images on its platform. The moderation team reports that some images containing nudity are being approved, while harmless images of sculptures are being rejected. The company wants to review the specific labels and confidence scores that Rekognition assigned to each image before deciding whether to appeal. Which action should the team take to obtain this information?
⚠ Common exam trap
It's easy for candidates to confuse API activity logging in AWS CloudTrail with the actual moderation label output, leading teams to expect CloudTrail to contain confidence scores it never records.
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
✓
Enable Amazon Rekognition content moderation and inspect the ModerationLabels returned in the DetectModerationLabels response
DetectModerationLabels returns a ModerationLabels list where each entry includes a category name and a confidence score, giving the moderation team the exact evidence behind each decision. This supports appeals and threshold tuning. Custom Labels would replace the classifier, A2I adds human review rather than exposing existing scores, and CloudTrail logs API activity without capturing label content.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Enable AWS CloudTrail data events on the Amazon Rekognition API to capture label details
Why it's wrong here
CloudTrail data events log API calls such as who invoked DetectModerationLabels and when, but they do not record the returned moderation labels or confidence values. The audit trail is about access and activity, not classification content. Using CloudTrail would not reveal why specific images were approved or rejected, so it does not satisfy the review requirement.
- ✗
Use Amazon Augmented AI (A2I) to route every image to human reviewers before moderation
Why it's wrong here
Amazon A2I creates human review workflows for low-confidence predictions, which can improve accuracy over time. However, it does not expose the specific labels and confidence scores for already-processed images, and routing every image to humans is costly and slow. The team needs visibility into existing moderation output, not a new human-in-the-loop pipeline for all traffic.
- ✗
Configure Amazon Rekognition Custom Labels to train a new moderation model on the rejected images
Why it's wrong here
Custom Labels trains a bespoke model using labeled images, which is a larger project than inspecting existing moderation output. It would not explain why the current moderation decisions were made and requires a labeled dataset plus training time. The immediate need is to review the labels and confidence scores already produced, not to build a replacement classifier.
- ✓
Enable Amazon Rekognition content moderation and inspect the ModerationLabels returned in the DetectModerationLabels response
Why this is correct
Amazon Rekognition's DetectModerationLabels API returns ModerationLabels with a name and a confidence score for each detected category, such as Explicit Nudity or Suggestive. Reviewing these labels and scores lets the team see exactly why an image was approved or rejected and supports a documented appeal process. This directly addresses the need to inspect per-image classification details.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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.