AIF-C01 Guidelines for Responsible AI — Timed 10 Questions

Question 1

Which TWO actions are most aligned with responsible AI practices when deploying a model that makes decisions affecting individuals? (Choose 2)

Accepted Answer

Continuously monitor the model for fairness metrics. Options B and E are correct. Continuous monitoring for fairness metrics helps detect and mitigate bias over time. Providing meaningful explanations for model decisions supports transparency and accountability. Option A is wrong because collecting data without quality checks can introduce bias and reduce data quality, undermining fairness. Option C is wrong because a homogeneous development team can lead to blind spots and lack of diverse perspectives, which is contrary to responsible AI practices. Option D is wrong because using the most complex model does not necessarily ensure fairness and may harm explainability and transparency.

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Collect as much data as possible without quality checks

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Ensure the development team is homogeneous to avoid conflicts

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Use the most complex model available for maximum accuracy

Question 2

A healthcare company must train a model on sensitive patient data while complying with privacy regulations. They want to add noise to the training process to prevent re-identification. Which technique should they implement?

Accepted Answer

Differential privacy. Differential privacy is the correct technique because it adds calibrated noise to the training process (e.g., via gradient clipping and noise injection in stochastic gradient descent) to ensure that the model's outputs do not reveal whether any individual's data was included in the training set. This provides a formal mathematical guarantee (ε-differential privacy) that limits the risk of re-identification, which is essential for complying with privacy regulations like HIPAA or GDPR when training on sensitive patient data.

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k-anonymity

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Federated learning

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Homomorphic encryption

Question 3

Which of the following is a key principle of responsible AI according to AWS?

Accepted Answer

Transparency. Transparency is a core principle of responsible AI according to AWS, meaning that customers should be able to understand how an AI system makes decisions, including its inputs, outputs, and limitations. AWS emphasizes this through features like model cards in Amazon SageMaker, which document model performance, intended uses, and biases. This principle ensures that AI systems are explainable and auditable, building trust with users.

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Complexity

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Speed

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Profitability

Question 4

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 5

Which TWO actions should a data scientist take to evaluate fairness of a binary classification model using Amazon SageMaker Clarify? (Choose two.)

Accepted Answer

Use post-training bias metrics like Difference in Positive Proportions. Amazon SageMaker Clarify provides post-training bias metrics such as Difference in Positive Proportions (DPPL) to measure disparities in model predictions across sensitive groups. These metrics quantify whether the model's predictions are fair after training, which is a key step in evaluating fairness for a binary classification model.

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Ensure the training dataset is balanced by resampling

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Generate SHAP values for feature importance

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Run a data quality monitoring job on unlabeled data

Question 6

Which TWO techniques provide interpretability for machine learning models at a local (per-prediction) level? (Choose two.)

Accepted Answer

SHAP values. SHAP (SHapley Additive exPlanations) values are a game-theoretic approach that explains individual predictions by computing the contribution of each feature to the prediction. For a given instance, SHAP assigns an importance value to each feature, showing how much it pushed the prediction away from the average. This makes it a local interpretability method because the explanation is specific to that single prediction, not the model overall.

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Partial dependence plots

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Confusion matrix

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Permutation feature importance

Question 7

Refer to the exhibit. A developer is reviewing CloudWatch Logs for a deployed model and notices the same input appears multiple times with slightly different probabilities. What responsible AI concern does this pattern suggest?

Accepted Answer

The model is not robust; it produces inconsistent predictions for the same input.. The pattern of the same input producing slightly different probabilities indicates that the model's predictions are not deterministic for identical inputs. This violates the principle of robustness in responsible AI, which requires that a model should produce consistent outputs for the same input under the same conditions. Inconsistent predictions for identical inputs undermine trust and reliability, making the model non-robust.

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The model is overfitting to the training data.

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The model is exhibiting bias against a demographic group.

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The input data is drifting from the training distribution.

Question 8

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 9

A social media company uses Amazon Comprehend to moderate user comments. They want to avoid censoring legitimate speech while catching hate speech. Which approach aligns with responsible AI governance?

Accepted Answer

Implement a human-in-the-loop review for borderline cases. A human-in-the-loop (HITL) review for borderline cases aligns with responsible AI governance by balancing automated detection with human judgment. Amazon Comprehend can flag comments with moderate confidence scores (e.g., 0.5–0.9) for manual review, ensuring that ambiguous or context-dependent hate speech is not censored while still catching clear violations. This approach mitigates false positives and respects free expression, which is a core tenet of responsible AI.

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Use multiple models and average their scores

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Use a single model with high confidence threshold

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Rely solely on automated filtering

Question 10

Which TWO actions help ensure fairness in an AI system deployed on AWS? (Select two.)

Accepted Answer

Train the model on a representative dataset. Training an AI model on a representative dataset ensures that the data reflects the diversity of the real-world population the system will serve. A non-representative dataset can lead to biased predictions, as the model may underperform or discriminate against underrepresented groups. This is a foundational step in responsible AI to mitigate fairness issues before model deployment.

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Enable AWS CloudTrail for audit

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Use a single validation set

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Encrypt data at rest using AWS KMS