AIF-C01 Guidelines for Responsible AI Practice Test 2 — 15 Questions

Question 1

A data scientist is using Amazon SageMaker to train a model and wants to understand the contribution of each feature to individual predictions. Which technique should they use to generate local explanations?

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

Accepted Answer

SHAP values. SHAP (SHapley Additive exPlanations) values are the correct choice because they provide local explanations by decomposing a prediction into the additive contribution of each feature, based on cooperative game theory. This allows the data scientist to understand exactly how each feature influenced a specific individual prediction, unlike global methods that summarize behavior across the entire dataset.

Answer

Permutation feature importance

Answer

Global feature importance

Answer

Partial dependence plots

Question 2

A company deploys a deep learning model for image classification using Amazon SageMaker. They are concerned about adversarial attacks that could misclassify images with small perturbations. Which of the following is the most effective approach to improve model robustness?

Accepted Answer

Apply adversarial training. Adversarial training is the most effective approach because it explicitly augments the training dataset with adversarial examples—inputs crafted with small, intentional perturbations designed to fool the model. By training on these perturbed samples, the model learns to recognize and resist such attacks, directly improving its robustness against adversarial perturbations in image classification tasks on SageMaker.

Answer

Reduce training data size

Answer

Use early stopping during training

Answer

Increase model complexity

Question 3

A large enterprise has multiple teams deploying ML models on AWS. To ensure governance and accountability, they need to enforce that all models pass a fairness review before production deployment. Which SageMaker feature should they use to implement this approval workflow?

Accepted Answer

SageMaker Model Registry. SageMaker Model Registry is the correct choice because it provides a centralized catalog for managing ML models, including versioning, approval status, and metadata. It supports approval workflows by allowing you to define model groups, set approval statuses (e.g., PendingApproval, Approved, Rejected), and integrate with CI/CD pipelines to enforce that only approved models are deployed to production.

Answer

SageMaker Studio

Answer

SageMaker Experiments

Answer

SageMaker Model Monitor

Question 4

An e-commerce company uses an Amazon Lex chatbot to handle customer inquiries. They want to implement human oversight for sensitive interactions, such as when the chatbot cannot provide a confident response. Which AWS service should they integrate?

Accepted Answer

Amazon Augmented AI (A2I). Amazon Augmented AI (A2I) is the correct service because it provides built-in human review workflows for ML predictions, allowing you to route low-confidence responses from Amazon Lex to human reviewers for oversight. This directly addresses the requirement for human oversight on sensitive interactions where the chatbot cannot provide a confident response.

Answer

Amazon Rekognition

Answer

Amazon Comprehend

Answer

Amazon SageMaker Ground Truth

Question 5

Which of the following is NOT one of the core principles of responsible AI as defined by AWS?

Accepted Answer

Profitability. Profitability is not one of the core principles of responsible AI as defined by AWS. AWS defines six core principles: Fairness, Transparency, Robustness, Privacy, Security, and Explainability. Profitability is a business objective, not a principle for ensuring ethical and trustworthy AI systems.

Answer

Transparency

Answer

Fairness

Answer

Robustness

Question 6

A financial services company must comply with regulatory requirements that mandate explainability of credit scoring models. They have deployed a model using SageMaker and need to generate reports showing feature importance for each prediction. Which combination of services should they use to automate this?

Accepted Answer

SageMaker Clarify + SageMaker Pipelines. SageMaker Clarify provides built-in explainability capabilities, including feature importance for individual predictions via SHAP values, which directly addresses the regulatory requirement for model explainability. SageMaker Pipelines automates the end-to-end workflow, allowing you to schedule and run Clarify processing jobs to generate reports on a recurring basis without manual intervention.

Answer

SageMaker Model Monitor + Amazon QuickSight

Answer

SageMaker Ground Truth + AWS Lambda

Answer

SageMaker Data Wrangler + SageMaker Studio

Question 7

A data science team is building a resume screening model and wants to ensure it does not exhibit gender bias. Which TWO actions are most effective for mitigating bias? (Choose TWO.)

Accepted Answer

Apply adversarial debiasing techniques during training.. Adversarial debiasing is a technique that trains a primary model to perform the main task (e.g., resume screening) while simultaneously training an adversarial model to predict the protected attribute (e.g., gender) from the primary model's outputs. The primary model is penalized when the adversary can successfully predict gender, forcing it to learn representations that are less correlated with gender. This directly reduces gender bias in the model's predictions without requiring the removal of potentially useful correlated features.

Answer

Use a more complex deep learning model.

Answer

Remove the gender attribute and all correlated features from the dataset.

Answer

Ensure the training dataset has equal numbers of male and female candidates.

Question 8

A company is deploying an AI-based diagnostic system in healthcare. Which THREE practices align with AWS responsible AI guidelines? (Choose THREE.)

Accepted Answer

Continuously monitor model performance for drift using SageMaker Model Monitor.. AWS Responsible AI guidelines emphasize continuous monitoring to detect and mitigate model drift, which ensures the model remains accurate and fair over time. SageMaker Model Monitor provides automated alerts for data quality, bias, and feature attribution drift, aligning with the principle of reliability and safety.

Answer

Deploy the model in production immediately after training without manual review.

Answer

Use only automated decision-making without any human oversight.

Question 9

A team is using Amazon Comprehend to analyze customer feedback for sentiment. They want to detect and mitigate potential bias against certain demographic groups. Which TWO approaches should they consider? (Choose TWO.)

Accepted Answer

Use SageMaker Clarify to compute bias metrics on the training data.. SageMaker Clarify is specifically designed to detect and measure bias in machine learning models and training data. It computes bias metrics such as class imbalance, difference in positive proportions, and other statistical indicators across demographic groups, enabling teams to identify and mitigate bias before deploying models into production.

Answer

Use AWS WAF to filter out biased comments.

Answer

Use AWS CloudTrail to audit API calls.

Answer

Use Amazon Rekognition to verify images.

Question 10

Refer to the exhibit. A data scientist runs SageMaker Clarify on a training dataset and receives the above JSON output. Which bias metric exceeds its threshold?

Accepted Answer

DPL. The JSON output shows DPL (Demographic Parity Difference) = 0.15, which exceeds the commonly used threshold of 0.10. DPL measures the difference in the probability of a favorable outcome between advantaged and disadvantaged groups; a value above 0.10 indicates significant bias. The other metrics (Label Imbalance = 0.05, Class Imbalance = 0.02) are below their respective thresholds, so only DPL triggers the violation.

Answer

Label Imbalance

Answer

Class Imbalance

Answer

All metrics exceed thresholds

Question 11

Refer to the exhibit. An AWS CloudTrail log shows the creation of an IAM policy for a SageMaker execution role. Which responsible AI concern does this configuration raise?

Accepted Answer

Lack of least privilege access control. The CloudTrail log shows the creation of an IAM policy for a SageMaker execution role. If this policy grants overly broad permissions (e.g., `s3:*` or `iam:PassRole` to all resources), it violates the principle of least privilege, which is a core responsible AI concern. Overly permissive roles can lead to unauthorized access to training data, models, or other AWS resources, undermining security and governance.

Answer

Insufficient training data

Answer

Violation of data residency requirements

Answer

Absence of model monitoring

Question 12

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.

Answer

To retrain the model weekly

Answer

To replace the model with a new version

Answer

To monitor data drift in training data

Question 13

A retail company is deploying a chatbot to handle customer inquiries. During testing, they notice the chatbot occasionally uses offensive language when responding to certain user inputs. Which responsible AI principle is being violated?

Accepted Answer

Fairness. The chatbot's use of offensive language in responses indicates a bias in the model's training data or behavior, which violates the Fairness principle. Fairness in responsible AI requires that systems do not discriminate against or harm individuals or groups based on protected characteristics, and generating offensive outputs directly undermines this. This is distinct from privacy (data protection), transparency (explainability), or accountability (responsibility for outcomes).

Answer

Privacy

Answer

Transparency

Answer

Accountability

Question 14

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. The most effective first step to address potential bias is to audit the training data for bias and review feature selection. This aligns with the AWS Responsible AI guidelines, which emphasize that bias mitigation must start with understanding the data and features before any model changes. Without auditing, you cannot identify the source of bias—whether it's in the labels, sampling, or feature correlations—making subsequent steps like retraining or deployment ineffective or harmful.

Answer

Immediately deploy the model and monitor for biased outcomes

Answer

Retrain the model with synthetic data generated from the original dataset

Answer

Remove all demographic features from the model

Question 15

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. Feature importance scores directly address explainability by showing which input factors (e.g., age, lab results) most influenced each prediction. This allows doctors to understand and trust the model's reasoning, which is a core requirement of responsible AI guidelines for high-stakes healthcare decisions.

Answer

Ensuring the model's overall accuracy exceeds 95%

Answer

Using a black-box ensemble model that achieves highest accuracy

Answer

Automating decisions without human review to reduce bias