CLF-C02 Cloud Concepts Practice Question

Which AWS service provides a managed workflow for human review tasks, allowing machine learning models to request human oversight for low-confidence predictions?

⚠ Common exam trap

Candidates often confuse Amazon Augmented AI (A2I) with Amazon SageMaker Ground Truth, because both involve human tasks, but Ground Truth is for labeling training data while A2I is for reviewing live predictions.

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) is the correct service because it provides a managed workflow for human review of machine learning predictions, specifically when models have low confidence. It integrates with services like Amazon Rekognition and Amazon Textract to automatically route low-confidence predictions to human reviewers, enabling a human-in-the-loop (HITL) process without custom infrastructure.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

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Amazon SageMaker Ground Truth
Why it's wrong here
Amazon SageMaker Ground Truth is a fully managed data labeling service for building accurate training datasets used in machine learning models. It supports human labeling through built-in workflows, private teams, and Mechanical Turk, but its purpose is to create ground truth labels for training and validation, not to review inferences from a deployed production model. Once the model is deployed, Ground Truth is not integrated to continuously monitor and route live predictions for human approval. This key distinction makes it the wrong answer for post-inference human review.
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Amazon Augmented AI (A2I)
Why this is correct
Amazon Augmented AI (A2I) is the AWS-managed service that creates human-in-the-loop workflows for reviewing machine learning predictions. It allows you to set confidence thresholds, automatically routing low-confidence or risky predictions to human reviewers via built-in or custom workflows. A2I integrates with Amazon SageMaker, Rekognition, and Textract, and can use Mechanical Turk, private workforces, or vendor teams as reviewer pools. This is the correct answer because it directly fulfills the requirement of adding human review to ML inference.
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Amazon Mechanical Turk
Why it's wrong here
Amazon Mechanical Turk is a crowdsourcing marketplace where requesters post Human Intelligence Tasks (HITs) and remote workers complete them for pay. While it can provide human workers, it is not a managed ML prediction review service—it lacks the built-in integration with ML models, confidence thresholds, and automated routing that define A2I. In fact, A2I can leverage Mechanical Turk as one of its workforces, but using Mechanical Turk alone would require you to manually build the entire verification pipeline. Hence, it's incorrect for this scenario.
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Amazon Rekognition Custom Labels
Why it's wrong here
Amazon Rekognition Custom Labels is a feature of Amazon Rekognition that allows you to train custom image classification and object detection models using your own labeled dataset. It is designed to create custom computer vision models, not to orchestrate human review of model predictions. While custom models can produce confidence scores, Rekognition Custom Labels has no built-in human-in-the-loop workflow to automatically route low-confidence results to reviewers. Therefore, it does not provide the requested human review capability.

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CLF-C02 Cloud Concepts Practice Question

Why each option matters

Option-by-option breakdown

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