AIF-C01 Guidelines for Responsible AI Practice Question

A retail company is deploying a machine learning model to analyze customer reviews and predict sentiment. The team wants to follow responsible AI guidelines to ensure fairness, transparency, and accountability. Which TWO actions should the team take? (Choose TWO.)

⚠ Common exam trap

AWS often tests the distinction between monitoring for operational drift (Model Monitor) and evaluating for ethical bias (Clarify), leading candidates to confuse Model Monitor's drift detection with fairness analysis.

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 evaluate bias in the training data.

Option B is correct because SageMaker Clarify is the AWS service specifically designed to detect potential bias in training data and models, providing bias metrics (such as class imbalance and disparate impact) that directly support the fairness pillar of responsible AI. Option E is correct because SageMaker Model Cards provide a structured way to document a model's intended use, limitations, evaluation results, and risk information, which directly supports transparency and accountability requirements. Option A is not correct because SageMaker Debugger focuses on training performance and convergence issues (tensor analysis, profiling), not on fairness, transparency, or accountability. Option C is not correct because SageMaker Model Monitor detects data and model drift and can trigger retraining, but it addresses operational model quality rather than the responsible AI goals of fairness and transparency. Option D is not correct because Amazon Rekognition is a computer vision service for image and video analysis and cannot detect PII in review text; Amazon Comprehend would be the appropriate text-based service.

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 SageMaker Debugger to optimize training performance.
Why it's wrong here
Debugger profiles training jobs to find vanishing gradients, bottlenecks and resource inefficiencies, which is an engineering performance concern unrelated to fairness, transparency or accountability. It is tempting because Debugger is the right tool when diagnosing why a deep learning job converges slowly, but responsible AI requires bias assessment, explainability and documented governance instead.
✓
Use SageMaker Clarify to evaluate bias in the training data.
Why this is correct
SageMaker Clarify detects statistical bias across training data and features, directly satisfying the fairness requirement by quantifying disparate impact before deployment. It also generates explainability reports showing which features drive predictions, supporting transparency and accountability for the sentiment model's outputs.
✗
Use SageMaker Model Monitor to automatically retrain the model when drift is detected.
Why it's wrong here
Model Monitor detects and alerts on data or concept drift; it does not retrain automatically, so this action does not deliver the transparency and accountability the guidelines require. It is tempting because drift monitoring is the correct control when maintaining production accuracy over time, but responsible AI here needs documented model cards, bias evaluation and human review.
✗
Use Amazon Rekognition to detect personally identifiable information (PII) in the review text.
Why it's wrong here
Amazon Rekognition is a service specifically designed for image and video analysis, not for processing textual data. Consequently, it cannot detect personally identifiable information (PII) within customer review text, which is the input for this scenario. This option is tempting because PII detection is crucial for responsible AI, and Rekognition *does* offer PII detection capabilities, but exclusively for visual content. It would be an appropriate choice if the task involved analysing images or video frames.
✓
Use SageMaker Model Cards to document the model's intended use, limitations, and evaluation results.
Why this is correct
SageMaker Model Cards satisfy the transparency and accountability requirements by recording intended use, limitations, and evaluation results in a structured artefact. This documentation lets reviewers and stakeholders audit how the sentiment model was assessed, directly addressing the stem's responsible AI constraint rather than merely improving model accuracy or performance.

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AIF-C01 Guidelines for Responsible AI Practice Question

Why each option matters

Option-by-option breakdown

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