Guidelines for Responsible AI

A financial services company is deploying a machine learning model to approve loans. They want to ensure that the model does not discriminate based on race or gender. Which AWS service or feature can help them detect bias in the model's predictions?

A company is deploying a generative AI application that creates marketing copy. They want to ensure the outputs do not include harmful or inappropriate content. Which AWS service can enforce content policies and filter undesirable outputs?

A company is developing a speech-to-text application for a diverse user base. To ensure inclusive design, they test the model with different accents and dialects. They find that error rates are higher for certain accents. Which responsible AI principle is most directly violated?

A healthcare AI system predicts patient diagnoses. The data collection process primarily samples from urban hospitals, leading to underrepresentation of rural populations. Which type of bias is this, and what is the most effective mitigation strategy?

A data scientist is using SageMaker Clarify to analyze a binary classification model for gender bias. The dataset has 80% male and 20% female applicants. The model predicts positive outcomes for 60% of males and 30% of females. Which fairness metric would directly capture this disparity in prediction rates?

According to AWS's responsible AI principles, which principle focuses on the idea that AI systems should produce consistent and reliable results even under unexpected conditions?

A data science team is using SHAP values to explain a complex model. They notice that for a particular prediction, the SHAP value for feature 'age' is +0.3. What does this indicate?

A machine learning team uses SageMaker Clarify to evaluate a model for bias. The dataset includes a feature 'ZipCode' that correlates strongly with income and race. The team is concerned about proxy discrimination. What is the MOST effective way to address this in the context of responsible AI?

A financial institution is developing a model to detect fraudulent transactions. They want to ensure the model is robust and does not exhibit bias. Which TWO actions should they take?

A data science team is developing a credit scoring model and wants to ensure it meets fairness requirements. They measure the model's disparate impact and find it exceeds the 80% rule (adverse impact ratio >0.8). Which THREE actions should they consider to mitigate this? (Choose three.)

A social media company deploys a content moderation model. They want to minimize the risk of over-censoring legitimate posts (false positives) while still catching harmful content. Which metric should they prioritize?

A company is deploying a large language model (LLM) for customer support. They want to reduce the risk of hallucinations. Which TWO approaches should they implement? (Choose two.)

A hospital is deploying an AI system to assist in diagnosing diseases from medical images. According to the EU AI Act, this system may be classified as high-risk. Which THREE requirements should the hospital address to comply with the EU AI Act for high-risk AI systems?

A social media platform uses an AI system to moderate content. They want to ensure that human reviewers can review decisions when the AI is uncertain. Which AWS service can be used to set up a human review workflow for AI predictions?

What is the primary purpose of a model card?

A financial institution is building a model to approve loan applications. They must comply with the EU AI Act, which classifies credit scoring as a high-risk AI system. Which requirement is the MOST likely to apply under the EU AI Act?

A model trained to predict credit risk shows that applicants from a certain zip code are disproportionately rejected, even though income and credit history are comparable. Which type of bias is MOST likely present?

An organization wants to document key information about their machine learning model, including intended use, performance metrics, training data, and ethical considerations. Which tool or practice should they adopt?

A healthcare organization uses an ML model to predict patient readmission risk. To comply with regulations, they need to explain individual predictions to clinicians. Which explainability technique provides local, model-agnostic explanations that are computationally efficient?

A company is using Amazon Bedrock to deploy a generative AI application. They want to implement guardrails to prevent the model from generating harmful or offensive content. Which feature of Bedrock Guardrails should they configure?

A company is developing a generative AI application for content creation. They want to ensure transparency as per responsible AI guidelines. Which THREE practices should they implement? (Choose three.)

A company is developing an LLM-powered application that generates investment advice. They are concerned about the model producing inaccurate or fabricated information. Which combination of techniques should they implement to minimize hallucinations?

A company wants to understand which features are most important for their model's predictions globally. They trained a gradient boosting model on tabular data. Which technique provides a global, model-agnostic measure of feature importance?

A company is developing an AI system that screens job applications. To comply with regulatory requirements, they need to provide explanations for each automated decision. Which explainability technique provides global feature importance across the entire dataset?

An AI practitioner is deploying a large language model (LLM) for a customer support application. They are concerned about hallucinations, where the model generates plausible but incorrect information. Which combination of techniques would be MOST effective to mitigate hallucinations?

An AI system is used to detect fraudulent transactions. The system has a high false positive rate for a certain demographic group. To ensure fairness and reduce false positives, which mitigation strategy should be considered?

An insurance company is using a machine learning model to approve claims. They want to ensure that the model's approval rate is similar across different demographic groups. Which fairness metric would directly measure whether the proportion of positive outcomes is equal across groups?

A bank is deploying a credit scoring model and must comply with regulatory requirements that decisions can be explained to customers. The model is a gradient boosting machine with hundreds of features. Which explainability technique should the team use to provide local explanations for individual loan decisions?

A company is deploying an LLM for generating marketing copy. They want to reduce the risk of hallucinations and ensure the content is factually accurate. Which TWO approaches should they implement?

Which of the following is a key principle of inclusive design in AI?

A team is building an AI chatbot that will be used by customers with visual impairments. Which design practice best supports inclusive accessibility for this user group?

A company uses an LLM to generate medical advice. They are concerned about hallucinations and want to implement safeguards. Which TWO techniques should they prioritize?

A company is developing an AI system to screen job applications. They want to ensure the system does not discriminate against candidates based on gender. The dataset used contains historical hiring decisions that may reflect past biases. Which type of bias is MOST likely present in this scenario?

An AI system is used to screen job applications. The team finds that the model has a higher false positive rate for male applicants than female applicants. Which fairness metrics should they compute to quantify this disparity? (Choose two.)

A company is developing an AI recruitment tool that screens candidates. They want to minimize bias and ensure compliance with emerging regulations like the EU AI Act. Which THREE measures should they implement? (Select THREE.)

A healthcare startup is using an LLM to summarize patient medical records. They are concerned about hallucinations where the model may invent symptoms or treatments. Which combination of techniques should they implement to reduce hallucinations while maintaining accuracy?

A financial services company is deploying a machine learning model to approve loan applications. To comply with regulatory requirements, they must ensure the model does not discriminate based on race. They have historical data that may contain bias. Which AWS service can help detect and measure bias in the dataset and model predictions?

A data scientist is using Amazon SageMaker Clarify to generate a bias report for a binary classification model. They want to understand which features most influence the model's predictions. Which feature of Clarify should they use?

A data scientist is using Amazon SageMaker Clarify to generate a model explainability report. They want to include both global and local feature importance. Which TWO techniques does SageMaker Clarify support for these purposes?

A financial institution uses a machine learning model to approve loan applications. To comply with regulatory requirements, they need to explain individual predictions. Which AWS service and feature combination should they use?

A company uses a text generation model to produce legal documents. They want to minimize the environmental impact of training and inference. Which THREE approaches should they consider?

A company wants to document their machine learning model's intended use, limitations, and ethical considerations. Which TWO practices should they adopt? (Choose two.)

A healthcare startup is deploying an AI system to assist in diagnosing skin conditions from images. They want to follow the NIST AI Risk Management Framework. Which THREE practices should they implement?

A company is developing an AI system for resume screening. They want to ensure fairness and reduce bias. Which THREE steps should they take in accordance with the NIST AI Risk Management Framework and AWS responsible AI principles?

A company builds an AI system that generates medical diagnoses. To ensure patient safety and allow oversight, the company wants a human to review all high-risk predictions before they are acted upon. Which AWS service should they use?

A healthcare startup is deploying an AI model to assist with diagnosis. They want to comply with the EU AI Act, which classifies medical AI as high-risk. Which THREE requirements must they fulfill? (Choose three.)

A company is developing an AI system that generates news articles. To comply with transparency regulations, they must clearly indicate when content is AI-generated. Which action should they take?

A company deploys a large language model to automatically generate product descriptions. They want to ensure customers are aware that the content is AI-generated, as part of transparency requirements. What should they implement?

A company is building a resume screening model and discovers that the training data contains only resumes from one gender, leading to biased predictions. Which type of bias does this represent, and what is the most effective mitigation strategy?

A company uses a generative AI model to create marketing copy. They want to ensure that customers know the content is AI-generated. Which practice directly addresses this transparency requirement?

A data scientist is using Amazon SageMaker Clarify to analyze a model and discovers that the model treats two different demographic groups differently when they should have similar outcomes. The data scientist wants to quantify this difference using a metric that compares the proportion of positive outcomes for each group. Which metric should be used?

A bank wants to use Amazon Augmented AI (A2I) to review high-value loan applications that require human judgment. Which workflow best implements human-in-the-loop review for these predictions?

A healthcare organization is deploying an AI system to assist in diagnosing diseases from medical images. They need to ensure the system is robust, safe, and subject to human oversight. Which TWO actions align with responsible AI guidelines? (Select TWO.)

A company is developing an AI system that transcribes medical consultations. To ensure privacy and security, they need to implement controls that protect patient health information (PHI). Which AWS service can help anonymize data before it is used for model training?

A company uses an LLM to summarize medical research papers. They are concerned about hallucinations. Which combination of techniques would most effectively reduce hallucinations in this context?

An e-commerce company uses an LLM to generate product descriptions. They observe that occasionally the model outputs factually incorrect information about products. What is the term for this phenomenon?

Which AWS service provides human review workflows to handle low-confidence predictions or high-risk decisions in an AI system?

An organization is required to provide transparency about AI-generated content. Which of the following is the best practice to comply with transparency requirements?

An AI practitioner is evaluating a text generation model and notices that the model sometimes produces plausible-sounding but factually incorrect statements. What is this phenomenon called?

A company is using an LLM to generate customer support responses. They want to reduce hallucinations and improve the accuracy of the responses. Which TWO approaches are most effective? (Select TWO.)

An organization wants to document their model's intended use, limitations, performance metrics, and ethical considerations. Which tool or practice is designed specifically for this purpose?

A financial services firm is deploying a loan approval model and must comply with the EU AI Act, which classifies credit scoring as a high-risk AI system. Which combination of actions is required for such high-risk systems under the regulation?

Which AWS service can be used to create human review workflows for high-risk AI predictions, ensuring a human-in-the-loop?

A company is using a machine learning model to predict employee turnover. The model's predictions are used to identify at-risk employees for retention efforts. The company wants to ensure that the model does not inadvertently discriminate against employees based on age. Which metric should be used to measure fairness across age groups?

A company deploys a machine learning model for resume screening. They want to measure whether the model selects candidates proportionally across different demographic groups. Which fairness metric is most appropriate?

A data science team wants to document and share their model's intended use, performance, and limitations with stakeholders. They also need to track the model's version and deployment history. Which TWO AWS services or features should they use?

A company wants to build a customer service chatbot that answers questions about their internal policy documents. The documents are updated monthly, and the team cannot afford to retrain a model each time. Which approach is MOST appropriate?

A healthcare organization uses an ML model to predict patient readmission risk. The model performs well overall but has significantly higher false negative rates for elderly patients. The team needs to mitigate this bias. Which step should they take FIRST?

An AI practitioner is training a resume screening model and discovers that the model has a significantly lower recall for female candidates compared to male candidates. Which type of bias is MOST likely present?

A company wants to build a customer service chatbot that answers questions about their internal policy documents. The documents are updated monthly, and the team cannot afford to retrain a model each time. Which approach is MOST appropriate?