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AIF-C01 Practice Question: A bank wants to use Amazon Augmented AI (A2I) to…
A bank wants to use Amazon Augmented AI (A2I) to review high-value loan applications that require human judgment. Which workflow best implements human-in-the-loop review for these 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
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Set up an A2I workflow with a confidence threshold, so low-confidence predictions are sent to human reviewers
Amazon A2I enables human review of low-confidence predictions or specific conditions. The best practice is to set a confidence threshold; predictions below that threshold are sent to human reviewers.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Set up an A2I workflow with a confidence threshold, so low-confidence predictions are sent to human reviewers
Why this is correct
Amazon A2I triggers human review when inference confidence falls below a configured threshold, routing only uncertain predictions to reviewers. This satisfies the bank's need for human judgment on high-value loan applications without reviewing every prediction, since A2I integrates this condition directly into the workflow.
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Route all loan applications to human reviewers for approval
Why it's wrong here
Routing every application to humans removes the automated prediction and its confidence-based trigger, defeating A2I's purpose of reviewing only low-confidence or high-value cases. Full manual review fits scenarios with no model in the loop at all.
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Use Amazon Mechanical Turk to review all predictions in real time
Why it's wrong here
Mechanical Turk is a public crowdsourcing marketplace, unsuitable for confidential loan data and lacking the review workflow, queues, and audit controls A2I provides. It tempts as a cheap human review source, but the correct approach uses A2I with a private workforce and human review workflow for sensitive predictions.
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Configure a private workforce in Amazon SageMaker Ground Truth
Why it's wrong here
A private workforce defines who reviews, not how A2I routes predictions; A2I needs a human review workflow with activation conditions and task UI to trigger review. Ground Truth workforces suit labelling datasets for training, not gating live inference.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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