easyMultiple Choice
AIF-C01 Practice Question: Which AWS service provides human review workflows…
Which AWS service provides human review workflows to handle low-confidence predictions or high-risk decisions in an AI system?
⚠ Common exam trap
The trap is confusing Amazon Augmented AI (A2I) with SageMaker Ground Truth — both involve humans, but A2I is for reviewing live predictions (human-in-the-loop), while Ground Truth is for labeling training data.
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
✓
Amazon Augmented AI (A2I)
Amazon Augmented AI (A2I) provides human review workflows that integrate with ML predictions, allowing organizations to route low-confidence predictions or high-risk decisions to human reviewers. It supports both Amazon-built integrations (e.g., Rekognition, Textract) and custom ML models, and it manages the review UI, workforce, and result consolidation. This is exactly the service designed for human-in-the-loop review of AI decisions.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
AWS Lambda
Why it's wrong here
Lambda executes code in response to events; it provides no review UI, workforce, or task-routing mechanism for flagging low-confidence predictions. It suits serverless compute glue, such as invoking a review API, but not the human review workflow itself.
- ✓
Amazon Augmented AI (A2I)
Why this is correct
Amazon Augmented AI provides built-in human review workflows, routing low-confidence inferences to human reviewers via configurable task types. It directly satisfies the stem's requirement to handle low-confidence predictions or high-risk decisions, unlike Rekognition or SageMaker Ground Truth alone.
- ✗
Amazon Bedrock
Why it's wrong here
Bedrock serves foundation models through APIs for inference and customisation; it exposes no human review queue or escalation workflow for uncertain predictions. It fits generating model outputs, not routing those outputs to people for adjudication.
- ✗
Amazon SageMaker Ground Truth
Why it's wrong here
Ground Truth labels training datasets and can route items to human labellers, but it is built for dataset annotation rather than runtime review of live low-confidence or high-risk inference results. It fits building labelled training corpora, not production human-in-the-loop decision handling.
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JA
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.