AIF-C01 Guidelines for Responsible AI Practice Test 3 — 15 Questions

Question 1

A company uses an AI system to screen job applications. The system was trained on resumes from previous hires, which predominantly came from a specific demographic. As a result, the system may unfairly filter out qualified candidates from other backgrounds. Which responsible AI practice should the company implement?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Implement bias detection metrics and monitor outcomes by demographic groups. Implementing bias detection metrics and monitoring outcomes by demographic groups directly addresses the risk of unfair filtering. This practice aligns with the responsible AI principle of fairness, requiring continuous evaluation of model outputs across protected groups to identify and mitigate demographic disparities in hiring decisions.

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Focus solely on improving the model's precision and recall

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Defer all screening decisions to a human recruiter

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Increase the size of the training dataset without regard to demographic composition

Question 2

A company is deploying a generative AI model that produces text summaries of legal documents. To comply with responsible AI guidelines, which of the following is the most critical to ensure transparency?

Accepted Answer

Informing users that the summaries are generated by AI. Transparency in responsible AI requires that users are clearly informed when they are interacting with AI-generated content, especially in high-stakes domains like legal document summarization. Option A directly addresses this by mandating disclosure, which builds trust and allows users to critically evaluate the output. Without such disclosure, users may mistakenly attribute human-level authority or accountability to the AI system.

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Ensuring the model does not reflect biases from training data

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Achieving high performance on summary quality metrics

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Guaranteeing the summaries are factually accurate

Question 3

An insurance company uses a machine learning model to adjust premiums. During a review, the model is found to be penalizing customers based on zip codes correlated with racial demographics, leading to potential discrimination. Which combination of actions best addresses this fairness issue while maintaining business value?

Accepted Answer

Re-engineer features to avoid proxies for protected attributes and rebalance training data. Re-engineering features to remove proxies for protected attributes (e.g., zip codes correlated with race) directly addresses the root cause of bias without discarding all geographic information. Rebalancing the training data helps mitigate skewed representations that could amplify discriminatory patterns, preserving business value by retaining useful predictive signals while aligning with fairness principles under the AIF-C01 Responsible AI guidelines.

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Remove the zip code feature from the model and retrain

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Continue using the model but add a disclaimer about potential bias

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Replace the model with a simpler linear model

Question 4

Which TWO actions are essential for ensuring accountability in AI systems according to AWS responsible AI guidelines?

Accepted Answer

Establish clear human oversight and decision-making authority. AWS responsible AI guidelines emphasize that human oversight is critical for accountability. Clear decision-making authority ensures that humans can review, override, or audit AI-driven outcomes, particularly in high-stakes scenarios. This aligns with the principle of 'human-in-the-loop' governance, where automated systems remain subordinate to human judgment. Option C is also correct because maintaining detailed documentation and version control for models ensures traceability and reproducibility, which are key to accountability. Documentation allows stakeholders to understand how models were developed, trained, and deployed, and version control tracks changes over time, enabling audits and error attribution. Options A, D, and E are incorrect: automating all decisions (A) removes necessary human judgment; removing human review (D) eliminates oversight; and sharing raw training data (E) raises privacy and compliance issues without directly supporting accountability.

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Automate all decisions to ensure consistency

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Remove all human review processes to eliminate bias

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Share raw training data publicly for transparency

Question 5

Which THREE practices are recommended for promoting robustness and security in AI systems?

Accepted Answer

Implement strong access controls and encryption for model artifacts. Implementing strong access controls (e.g., IAM policies, role-based access control) and encryption (e.g., AES-256 for data at rest, TLS 1.2+ for data in transit) protects model artifacts from unauthorized access, tampering, and exfiltration. This is a fundamental security practice that ensures the confidentiality and integrity of the AI system throughout its lifecycle.

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Deploy the model immediately after training without validation

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Remove logging and monitoring to improve performance

Question 6

A startup is developing a mobile app that uses facial recognition to verify user identity for account access. The app is intended for a global audience, but the training data predominantly includes images of light-skinned individuals. During beta testing, users with darker skin tones report frequent verification failures, while light-skinned users have a high success rate. The startup wants to release the app soon and needs to address this fairness issue without delaying the launch too much. The team has limited resources. Which approach should they take to most effectively mitigate the bias while meeting the launch timeline?

Accepted Answer

Collect more diverse training data and augment the existing dataset, then retrain the model. The root cause of the bias is a skewed training dataset that underrepresents darker skin tones. Collecting more diverse data and augmenting the existing dataset directly addresses the data imbalance, allowing the facial recognition model to learn robust features for all skin tones. Retraining the model on this enriched dataset is the most effective long-term fix that aligns with responsible AI principles, and with focused effort it can be completed within a reasonable timeline without introducing the risks of post-hoc patches.

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Apply a post-processing rule to increase acceptance rate for users with darker skin tones

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Lower the similarity threshold for all users to improve acceptance rates

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Defer verification for users with darker skin tones to manual human review

Question 7

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.)

Accepted Answer

Use SageMaker Clarify to evaluate bias in the training data.. Amazon SageMaker Clarify helps detect bias in training data and models, supporting the fairness pillar of responsible AI. Amazon SageMaker Model Cards allow teams to document model details such as intended use, limitations, and evaluation results, which promotes transparency and accountability. Together, these services address the key responsible AI principles of fairness, transparency, and accountability.

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Use SageMaker Debugger to optimize training performance.

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Use SageMaker Model Monitor to automatically retrain the model when drift is detected.

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Use Amazon Rekognition to detect personally identifiable information (PII) in the review text.

Question 8

Refer to the exhibit. A data scientist used SageMaker Clarify to evaluate bias in a binary classification model predicting loan approval. The exhibit shows bias metrics for the female facet. What does the analysis indicate about the model's impact on the female group?

Accepted Answer

The model has a post-training accuracy difference indicating a negative bias against the female group.. The exhibit shows a post-training accuracy difference metric (e.g., difference in positive predicted values or accuracy) that is negative for the female facet, indicating the model's predictions are less accurate for the female group compared to the overall or male group. This negative difference signifies a bias against the female group, as the model performs worse for them after training. SageMaker Clarify computes such metrics to detect disparate impact, and a negative value here directly points to adverse treatment.

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The metrics are within acceptable thresholds, so no action is needed.

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The model shows a high positive bias toward the female group.

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The model exhibits a pre-training class imbalance but no post-training bias.

Question 9

A healthcare company uses Amazon SageMaker to train a model that predicts patient readmission risk based on electronic health records (EHRs) stored in Amazon HealthLake. The training dataset contains 2 million records from the past three years, with a significant gender imbalance: 70% male and 30% female. The model achieved high overall accuracy, but further analysis using SageMaker Clarify revealed that the precision for female patients is 0.65 while for male patients it is 0.88. Additionally, the model's false positive rate for female patients is significantly higher. The company must comply with healthcare regulations that require fairness and non-discrimination. The data science team has already used SageMaker Data Wrangler for initial preprocessing and SageMaker Clarify for bias detection. They need to take immediate action to mitigate the bias before deploying to production. Which course of action should the team take?

Accepted Answer

Use SageMaker Clarify's bias mitigation feature to apply reweighing techniques and retrain the model with adjusted sample weights.. To use SageMaker Clarify's built-in bias mitigation technique (reweighing) as it directly addresses the disparity in precision and false positive rates between male and female patients by adjusting sample weights during training. SageMaker Clarify's bias mitigation feature is designed for this purpose. Option B: Generating SHAP values explains predictions but does not mitigate bias. Option C: SMOTE addresses class imbalance but not fairness in terms of group accuracy disparity; it may even worsen bias by oversampling without considering fairness. Option D: SageMaker Model Monitor is for detecting drift in production data, not for mitigating bias during training. Therefore, Option A is the best choice.

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Use SageMaker Clarify to generate SHAP values and adjust the model's feature importance by removing biased features.

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Use SMOTE (Synthetic Minority Oversampling Technique) to balance the training dataset before retraining.

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Use SageMaker Model Monitor to detect feature drift and automatically retrain the model with updated data.

Question 10

A financial services company uses Amazon Rekognition to verify customer identities. To ensure responsible AI practices, which measure should the company prioritize?

Accepted Answer

Regularly audit the model for demographic bias. Regularly auditing the model for demographic bias is a core responsible AI practice, especially for identity verification systems where biased outcomes could lead to unfair treatment of certain customer groups. Amazon Rekognition's facial analysis and comparison features must be tested across diverse demographics to ensure equitable performance, as bias can arise from imbalanced training data or algorithmic artifacts.

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Use only black-box models to protect intellectual property

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Increase model complexity to improve accuracy

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Minimize the amount of training data collected

Question 11

A healthcare startup deploys a model to predict patient readmission risk using Amazon SageMaker. After deployment, the model shows higher false-positive rates for a specific age group. What is the most responsible first step?

Accepted Answer

Use Amazon SageMaker Clarify to detect bias in predictions. Amazon SageMaker Clarify is purpose-built for detecting bias in ML models and data. It provides bias metrics (e.g., Difference in Positive Proportions in Predicted Labels, Disparate Impact) that can quantify whether the model's predictions are systematically skewed against a specific age group. This is the most responsible first step because it objectively measures the bias before any corrective action is taken.

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Increase the prediction threshold for the affected group

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Retrain the model with more data from the affected group

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Immediately retire the model to prevent harm

Question 12

A company uses an AI system to automate loan approvals. The model uses demographic features and achieves high accuracy, but the company wants to ensure compliance with responsible AI guidelines. Which practice best balances performance and fairness?

Accepted Answer

Remove sensitive attributes and monitor for proxy bias. Removing sensitive attributes (e.g., race, gender) from the training data directly addresses fairness by preventing the model from explicitly using these features. However, simply removing them is insufficient; monitoring for proxy bias (e.g., zip code or income correlating with race) is critical to ensure the model does not inadvertently learn discriminatory patterns through correlated features. This approach balances performance by retaining predictive power from non-sensitive features while actively auditing for fairness violations.

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Use demographic features but with minimal monitoring

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Use a complex black-box model and rely on post-hoc explanations

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Optimize the model solely for accuracy on historical data

Question 13

A retail company uses a recommendation system that occasionally suggests inappropriate products to minors. Which responsible AI practice should be applied?

Accepted Answer

Implement human review of flagged recommendations. The correct practice is to implement human review of flagged recommendations. This aligns with the responsible AI principle of accountability, where automated systems must have oversight mechanisms to catch and correct inappropriate outputs, especially when minors are involved. Human-in-the-loop (HITL) validation ensures that edge cases or subtle context (e.g., age-inappropriate product suggestions) are caught before they reach end users, rather than relying solely on automated filters or feedback loops.

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Rely solely on user feedback to improve

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Disable the recommendation system entirely

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Increase the volume of training data

Question 14

A company uses Amazon Comprehend to analyze customer sentiment. They discover the model performs poorly on text with slang from underrepresented groups. What is the most responsible action?

Accepted Answer

Collect more representative training data including slang. The core principle of responsible AI requires that models be trained on data that is representative of the populations they serve. Amazon Comprehend's sentiment analysis is a supervised machine learning model; its poor performance on slang from underrepresented groups indicates a training data bias. Collecting more representative training data, including that slang, directly addresses the root cause by enabling the model to learn the linguistic patterns of those groups, improving fairness and accuracy without restricting access or masking the problem.

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Restrict model use to only standard English

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Remove slang from input before inference

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Adjust the confidence threshold only for those groups

Question 15

A bank uses an AI system to detect fraudulent transactions. The model has high precision but low recall for small transactions, potentially missing fraud. Which approach aligns with responsible AI?

Accepted Answer

Tune the model to achieve an acceptable balance between recall and precision. Responsible AI requires balancing competing objectives like precision and recall to align with ethical principles and business needs. In fraud detection, high precision with low recall means many fraudulent transactions are missed, which can lead to significant financial losses and erode customer trust. Tuning the model to achieve an acceptable trade-off ensures that the system is both effective and fair, minimizing harm while maintaining operational viability.

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Send all flagged transactions to customers for confirmation

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Focus only on precision to minimize false positives

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Increase the detection threshold to reduce false positives