AIF-C01 Guidelines for Responsible AI Practice Test 5 — 15 Questions

Question 1

A financial institution uses a machine learning model to approve loan applications. The model is trained on historical data that includes biased lending practices. What is the most effective first step to address potential bias?

Accepted Answer

Audit the training data for bias and review feature selection. Before deploying, it is critical to evaluate the training data for bias. Auditing the data for representation and fairness helps identify and mitigate bias early. Post-deployment monitoring is secondary, and simply retraining without review may perpetuate bias. Excluding demographic data might ignore important fairness dimensions.

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Immediately deploy the model and monitor for biased outcomes

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Retrain the model with synthetic data generated from the original dataset

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Remove all demographic features from the model

Question 2

A healthcare organization uses an AI model to predict patient readmission risks. The model's predictions are used by doctors to allocate follow-up care. The organization wants to ensure compliance with responsible AI guidelines. Which practice best supports explainability?

Accepted Answer

Providing feature importance scores for each prediction. Providing feature importance scores helps doctors understand why the model made a specific prediction, aligning with explainability. Removing humans oversimplifies accountability. Using a black-box model reduces explainability. Relying solely on accuracy ignores interpretability.

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Ensuring the model's overall accuracy exceeds 95%

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Using a black-box ensemble model that achieves highest accuracy

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Automating decisions without human review to reduce bias

Question 3

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?

Accepted Answer

Implement bias detection metrics and monitor outcomes by demographic groups. To mitigate bias, the company should measure and monitor the system's impact across demographic groups. This aligns with fairness metrics. Using more data without addressing bias may not help. Relying on human review is good but does not guarantee systematic fairness. Focusing only on performance ignores fairness.

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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 4

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 requires that users know when they are interacting with AI. Disclosing that the summaries are AI-generated is fundamental. Accuracy, bias mitigation, and performance are important but do not directly address transparency about AI use.

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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 5

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. The most effective approach is to re-engineer features to remove proxy variables for protected attributes (like race) AND to rebalance the training data to ensure fair representation. Simply removing zip code may not eliminate proxies, and ignoring the issue is not acceptable. Using a different model without data changes may not solve the problem.

Answer

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 6

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. Accountability requires clear ownership and documentation. Establishing human oversight and maintaining version control of models are key. Removing humans reduces accountability. Using only automated decisions removes oversight. Sharing raw data broadly may violate privacy.

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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 7

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

Accepted Answer

Implement strong access controls and encryption for model artifacts. Robustness and security involve testing for adversarial inputs, monitoring data drift, and implementing secure access controls. Using unvalidated models is risky. Removing logging reduces traceability.

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Deploy the model immediately after training without validation

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Remove logging and monitoring to improve performance

Question 8

Which TWO of the following are key components of a responsible AI governance framework?

Accepted Answer

Develop and enforce AI ethics policies and standards. Option A is correct because a responsible AI governance framework must include the development and enforcement of AI ethics policies and standards to ensure alignment with societal values, fairness, and accountability. These policies guide the design, deployment, and monitoring of AI systems, embedding ethical principles such as transparency, privacy, and non-discrimination into the AI lifecycle. Without such policies, organizations risk deploying AI that violates ethical norms or regulatory expectations.

Answer

Focus solely on compliance with legal regulations

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Minimize human involvement in AI lifecycle decisions

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Deploy AI models as black boxes to avoid scrutiny

Question 9

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 most effective approach is to collect additional training data representing diverse skin tones and augment the dataset, then retrain the model. This directly addresses the data imbalance. Applying a post-processing rule without retraining may not fix the underlying model bias. Deferring to humans is a temporary workaround and does not scale. Reducing the threshold for all users could increase false positives and may not be acceptable.

Answer

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 10

A hospital uses an AI system to prioritize patients for organ transplant based on predicted survival rates. The system was trained on historical data that includes socioeconomic factors. A review reveals that the system systematically assigns lower priority to patients from lower-income neighborhoods, even when medical urgency is similar. The hospital's ethics board demands an immediate remedy. The data science team is small and must act quickly. What should the hospital do to address this fairness issue most effectively?

Accepted Answer

Retrain the model with only medically relevant features, after removing socioeconomic factors and correlated proxies. The best course is to retrain the model using only medically relevant features, removing socioeconomic factors and correlated proxies. This directly addresses the source of bias. Adding a penalty for low-income patients is artificial and may not reflect medical reality. Relying solely on human review delays the issue and introduces potential inconsistency. Using a different model without data changes may not eliminate bias.

Answer

Discontinue the AI system and have all prioritization done by a human committee

Answer

Apply a re-weighting penalty to boost priority for low-income patients

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Use a different model type, such as a random forest instead of gradient boosting, on the same data

Question 11

A large e-commerce company uses a recommendation system to suggest products to customers. Recently, a data scientist noticed that the model's recommendations for high-value luxury items are predominantly shown to users in affluent zip codes, while users in less affluent areas rarely see these items, even if they have searched for them. The company is concerned about fairness and wants to ensure all customers have equal access to recommendations regardless of location. The current model uses collaborative filtering on historical purchase data. The team needs to modify the system without sacrificing overall recommendation accuracy. Which action best addresses the fairness concern while maintaining performance?

Accepted Answer

Remove zip code and any income-correlated features from the training data. The most effective approach is to ensure the model does not use zip code or any feature correlated with income as a direct or indirect input. This removes the proxy for socioeconomic status. Simply equalizing recommendation frequency artificially may hurt relevance. Personalizing based on search history is already being done but zip code bias remains. Adding more training data may not help if the bias is in the features.

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Randomly show luxury recommendations to a subset of users regardless of their behavior

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Add more training data from less affluent areas to balance the dataset

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Implement a separate recommendation pipeline for luxury items based only on search history

Question 12

A government agency is deploying an AI system to detect fraudulent benefit claims. The system uses a neural network trained on historical claims data, which includes a disproportionate number of false positives from a particular ethnic group due to historical over-policing. The agency must ensure the system does not perpetuate discrimination. They have a rigorous testing procedure but limited budget. The project lead wants to balance fairness with detection performance. Which combination of steps should they prioritize?

Accepted Answer

Rebalance the training data to have equal representation across groups and evaluate using a fairness metric like equal opportunity. The most effective approach is to rebalance the training data to be more representative and to use a fairness metric, such as equal opportunity, during evaluation. This directly addresses the data bias and quantifies fairness. Excluding race features may still leave proxies. Using a simpler model may not eliminate bias if data is biased. Post-hoc explanations help understand bias but do not fix it.

Answer

Remove the race feature from the model and rely on performance metrics alone

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Replace the neural network with a logistic regression model retrained on the same data

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Use a post-hoc explainability tool to identify biased predictions and manually override them

Question 13

A financial services company uses a machine learning model to automatically reject credit card transactions suspected of fraud. The model was trained on transaction data from the past two years. Over the last three months, the model's false positive rate has increased significantly, causing legitimate transactions to be declined and leading to customer complaints. The company needs to restore the model's accuracy quickly. Initial analysis shows that the distribution of transaction amounts and locations has shifted compared to the training period. The data science team is under pressure to deploy an update within a week. Which approach should they take to most effectively address the issue while adhering to responsible AI guidelines?

Accepted Answer

Retrain the model using only the most recent three months of transaction data and evaluate on current distribution. The most effective approach is to retrain the model using recent data (last three months) to adapt to the distribution shift, and carefully evaluate for any new biases that may emerge. This directly addresses the drift. Simply adjusting the threshold may not capture new fraud patterns. Using an ensemble of old and recent models could be complex and may not fully adapt. Deploying a simple rule-based system would be a step backward in capability.

Answer

Deploy a rule-based system with fixed rules for fraud detection

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Adjust the decision threshold to reduce false positives without retraining

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Build an ensemble model that combines predictions from the old model and a new model trained on recent data

Question 14

A media company uses a generative AI model to automatically create image captions for user-uploaded photos. During quality assurance, testers discover that the model sometimes generates captions that include stereotypes based on gender and race, even when the photos do not contain people. For example, a photo of a kitchen produces captions like 'woman cooking,' and a photo of a sports car generates 'man driving.' The company wants to launch the feature soon but recognizes the reputational risk. They have a limited budget and need to implement a solution that reduces harmful stereotypes without overly restricting the captions' creativity. The team has access to the model's training data, which is a large public dataset of image-caption pairs. Which approach should the team prioritize?

Accepted Answer

Filter the training data to remove or downweight pairs with stereotypes, then fine-tune the model. To reduce stereotypical associations, the most effective approach is to filter and rebalance the training data by removing or downweighting caption pairs that contain stereotypes, and then fine-tune the model. This directly addresses the data source of bias. Adding a post-hoc filter may catch some stereotypes but could also remove valid captions. Using a different model without data changes may not help. Limiting to object-only descriptions reduces creativity and may not meet product goals.

Answer

Replace the generative model with a simpler classification model that only describes objects

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Use a different pre-trained generative model that is larger and more accurate

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Add a post-processing filter that checks captions for known stereotype patterns and blocks them

Question 15

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.. Option A: SageMaker Clarify detects bias in training data, which is a core fairness practice. Option C: SageMaker Model Cards document the model's intended use, limitations, and evaluation results, promoting transparency and accountability. Option B: Model Monitor tracks data drift, not directly a responsible AI practice. Option D: Rekognition is for image moderation, not relevant for text sentiment. Option E: Debugger optimizes training, not responsible AI.

Answer

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.