AIF-C01 Guidelines for Responsible AI Practice Test 1 — 15 Questions

Question 1

A company wants to ensure accountability for its machine learning models by tracking all changes to the model and its training data. Which AWS feature should they use?

Accepted Answer

AWS CloudTrail. AWS CloudTrail is the correct choice because it records API activity across AWS services, enabling governance, compliance, and operational auditing. For machine learning models, CloudTrail logs all changes to model artifacts, training data, and SageMaker resources by capturing calls to the SageMaker API, such as CreateTrainingJob, UpdateEndpoint, or PutModelPackageGroupPolicy. This provides a complete audit trail for accountability, as required by responsible AI guidelines.

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Amazon CloudWatch

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Amazon SageMaker Experiments

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AWS CodeCommit

Question 2

A security team is concerned about adversarial attacks on their image classification model deployed on Amazon SageMaker. They want to test robustness against carefully crafted inputs that cause misclassification. What approach should they use?

Accepted Answer

Generating adversarial examples using SageMaker Clarify. SageMaker Clarify includes built-in capabilities for generating adversarial examples to evaluate model robustness against inputs designed to cause misclassification. This directly addresses the security team's concern by simulating adversarial attacks, allowing them to measure and improve the model's resilience before deployment.

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Data augmentation on the training set

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A/B testing between two similar models

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SageMaker Model Monitor with adversarial drift

Question 3

Refer to the exhibit. A team is creating an IAM policy for a SageMaker notebook user. The user needs to access training data in an S3 bucket and create models. Which responsible AI concern is most relevant to this policy?

Accepted Answer

The policy grants overly broad permissions, violating the principle of least privilege.. The IAM policy grants the SageMaker notebook user overly broad permissions (e.g., s3:* or sagemaker:*), which violates the principle of least privilege. This is a core responsible AI concern as it increases the risk of unauthorized access or accidental modification of training data and models. The policy should restrict actions to only those necessary for the user's specific role, such as s3:GetObject for specific buckets and sagemaker:CreateModel with resource constraints.

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The policy does not enforce encryption for the notebook.

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The policy does not restrict which S3 buckets the user can read.

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The policy does not include a condition for model explainability.

Question 4

A financial services company deploys a generative AI chatbot for customer support. They want to prevent the chatbot from generating harmful or misleading information. Which AWS service can help monitor and filter responses?

Accepted Answer

Amazon Augmented AI (A2I) with human review. Amazon Augmented AI (A2I) enables human review of machine learning predictions, making it suitable for monitoring and filtering chatbot responses for harmful or misleading content. Other options: Amazon GuardDuty is a threat detection service, not for content filtering. AWS WAF protects web applications from common web exploits. Amazon Comprehend is a natural language processing service for insights, but not designed for real-time filtering with human review.

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Amazon GuardDuty

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AWS WAF

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Amazon Comprehend

Question 5

An e-commerce company uses a recommendation system built with Amazon Personalize. They want to explain to customers why certain products are recommended. Which AWS service can provide model explanations?

Accepted Answer

Amazon SageMaker Clarify. Amazon SageMaker Clarify is the correct choice because it provides model explainability features, including feature importance and SHAP-based explanations, which can be used to interpret why Amazon Personalize recommends specific products. This aligns with the requirement to explain recommendations to customers, as SageMaker Clarify integrates with Personalize to generate human-readable explanations for model predictions.

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AWS Config

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AWS CloudTrail

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Amazon Detective

Question 6

A government agency uses Amazon Rekognition for identity verification. They want to ensure the model is robust against adversarial attacks. What should they do?

Accepted Answer

Train the model with adversarial examples. Training the model with adversarial examples (a technique known as adversarial training) directly improves the model's robustness against adversarial attacks by exposing it to perturbed inputs during training, forcing the model to learn decision boundaries that are less sensitive to small, malicious perturbations. This is a fundamental approach in building resilient AI systems, especially for sensitive tasks like identity verification.

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Use SageMaker Model Monitor to detect adversarial inputs

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Implement input validation and use Amazon SageMaker Shadow Testing

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Apply AWS Shield Advanced

Question 7

Which THREE practices support transparency in AI systems? (Select three.)

Accepted Answer

Provide documentation of model limitations. Option C is correct because documenting a model's limitations—such as known failure modes, bias risks, and out-of-scope use cases—gives stakeholders the information needed to understand what the system can and cannot reliably do, which is a core element of transparency. Option D is correct because publishing model performance metrics (e.g., accuracy, precision/recall, fairness measures across subgroups) lets users and auditors verify how the system behaves rather than taking claims on trust. Option E is correct because allowing users to opt out of data collection gives them visibility and control over how their data feeds the AI system, supporting transparent data-handling practices. Option A is not among the marked correct answers here: while SHAP is a legitimate explainability technique, the question's intended transparency practices are documentation, metric publication, and user opt-out, so it does not belong to this answer set. Option B is also not marked correct because using open-source models exclusively is a licensing/procurement choice, not a transparency practice—proprietary models can still be transparent if their limitations and metrics are disclosed.

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Implement explainability methods like SHAP

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Use open-source models exclusively

Question 8

Refer to the exhibit. An AWS administrator sets up a SageMaker Model Monitor schedule for bias detection. What is the primary purpose of this configuration?

Accepted Answer

To generate weekly bias reports for the deployed endpoint. SageMaker Model Monitor's bias detection feature is specifically designed to detect and report bias in model predictions over time. By configuring a weekly schedule, the administrator ensures that the deployed endpoint's predictions are regularly analyzed for bias, and a weekly bias report is generated. This aligns with the responsible AI goal of monitoring for fairness, not retraining or replacing the model.

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To retrain the model weekly

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To replace the model with a new version

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To monitor data drift in training data

Question 9

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 10

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. Rebalancing the training data to ensure equal representation across groups directly addresses the root cause of the bias: historical over-policing led to disproportionate false positives for a particular ethnic group. Evaluating with a fairness metric like equal opportunity ensures that the true positive rate is similar across groups, which is critical for a fraud detection system where both fairness and detection performance matter. This approach balances fairness and performance without discarding the neural network's capability.

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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 11

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. Retraining the model using only the most recent three months of transaction data and evaluating on the current distribution directly addresses the data drift identified in the scenario. This approach restores accuracy by aligning the training data with the current distribution, and evaluating on current data ensures the model performs well on the live population, adhering to responsible AI principles of transparency and fairness.

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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 12

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 13

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 14

A media company deploys an Amazon Bedrock text-generation assistant that writes marketing copy. Legal requires the assistant to refuse requests for defamatory content and to avoid generating copyrighted passages, and it must record who submitted each prompt for audit. Which TWO AWS capabilities should the team implement to meet these responsible AI requirements? (Choose two.)

Accepted Answer

Enable AWS CloudTrail to log Bedrock API calls and use the logs to capture the identity of each prompt submitter. The requirements combine content-policy enforcement and auditability. Bedrock Guardrails applies denied-topic and content policies at inference time so the assistant refuses prohibited requests, while CloudTrail captures the calling identity for every Bedrock invocation, providing the audit record. Capacity, metrics, and sampling settings do not enforce content rules or attribute prompts to submitters.

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Create an Amazon CloudWatch dashboard that charts Bedrock invocation counts and latency percentiles

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Enable provisioned throughput for the foundation model to guarantee consistent capacity during peak hours

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Increase the model's temperature setting so the assistant produces more varied and creative marketing copy

Question 15

A financial services company in Europe is deploying a generative AI assistant that explains loan denials to applicants. Regulatory counsel requires that every applicant receive a plain-language reason for the decision, and that the company be able to demonstrate which model version produced each reason. The team already uses Amazon Bedrock with a foundation model and Amazon CloudWatch for monitoring. Which combination of AWS capabilities best satisfies these two requirements?

Accepted Answer

Use Amazon Bedrock model invocation logging for traceability, and design the assistant to generate explanations grounded in the input features using retrieval augmented generation from a curated decision-reason knowledge base.. The two regulatory needs are per-decision explainability and model-version traceability. Invocation logging in Amazon Bedrock records the model ID and the exact prompt and response for each call, which satisfies the audit trail. Retrieval augmented generation from a curated reason knowledge base forces the explanation to be grounded in the applicant's actual decision factors, producing plain-language justifications rather than free-form model output.

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Use AWS CloudTrail data events on the Amazon Bedrock endpoint and Amazon Comprehend sentiment analysis on the generated text to confirm explanations are appropriately worded.

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Enable model invocation logging in Amazon Bedrock to capture requests and responses, and use Amazon Bedrock Guardrails to enforce a denied-topics policy on the assistant.

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Deploy the foundation model on Amazon SageMaker and use SageMaker Model Monitor to detect explanation drift, then publish a model card describing the assistant's intended use.