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

Question 1

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 2

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 3

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.. Option A is correct because adversarial debiasing trains a predictor alongside an adversary that tries to detect the protected attribute (gender) from the predictor's outputs, forcing the model to learn representations that cannot discriminate by gender, which directly mitigates bias during training. Option D is correct because regularly auditing model predictions for disparate impact across genders (e.g., using metrics like demographic parity, equal opportunity, or the 80% rule) detects bias that may persist or emerge after deployment and enables corrective action. Option B is not correct because increasing model complexity with deep learning does not inherently reduce bias and can even amplify it by fitting spurious correlations in the data. Option C is not correct because simply removing the gender attribute and correlated features does not eliminate bias, since proxy variables and historical patterns can still encode gender information. Option E is not correct because equal representation of male and female candidates in the training set does not guarantee fairness, as bias can arise from label imbalance, feature correlations, or unequal outcomes despite balanced group sizes.

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 4

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 several practices for high-risk systems like healthcare diagnostics. Continuous monitoring with SageMaker Model Monitor helps detect data quality, bias, and feature attribution drift, supporting reliability and safety (B). Documenting the model's intended use, limitations, and performance with model cards promotes transparency and accountability (D). Implementing a human-in-the-loop review process using Amazon Augmented AI (A2I) ensures meaningful human oversight for high-risk predictions (E). In contrast, deploying without manual review (A) and relying solely on automated decisions (C) violate responsible AI principles requiring human oversight and validation.

Answer

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

Answer

Use only automated decision-making without any human oversight.

Question 5

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.. Option D is correct because SageMaker Clarify is the AWS service purpose-built to detect bias in datasets and models, computing metrics such as class imbalance (CI) and difference in proportions of labels (DPL) across demographic groups, which directly addresses measuring bias in the training data used for sentiment analysis. Option E is correct because Amazon Comprehend custom classification lets you train a custom model on your own labeled data, and ensuring that training data is balanced across demographic groups reduces the risk that the classifier learns and amplifies skewed associations, thereby mitigating bias in the resulting sentiment predictions. Option A is not appropriate because AWS WAF is a web application firewall that filters HTTP/S traffic against exploits like SQL injection and XSS, not a tool for detecting or mitigating bias in text sentiment. Option B is not appropriate because AWS CloudTrail only records API activity for auditing and governance, and does not analyze or reduce bias. Option C is not appropriate because Amazon Rekognition performs image and video analysis (such as facial detection and moderation), which is unrelated to detecting bias in textual customer feedback sentiment.

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 6

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 7

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 8

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 9

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 10

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

Question 11

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. Implementing bias detection metrics and monitoring outcomes by demographic groups directly addresses the risk of unfair filtering. This practice aligns with the responsible AI principle of fairness, requiring continuous evaluation of model outputs across protected groups to identify and mitigate demographic disparities in hiring decisions.

Answer

Focus solely on improving the model's precision and recall

Answer

Defer all screening decisions to a human recruiter

Answer

Increase the size of the training dataset without regard to demographic composition

Question 12

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 in responsible AI requires that users are clearly informed when they are interacting with AI-generated content, especially in high-stakes domains like legal document summarization. Option A directly addresses this by mandating disclosure, which builds trust and allows users to critically evaluate the output. Without such disclosure, users may mistakenly attribute human-level authority or accountability to the AI system.

Answer

Ensuring the model does not reflect biases from training data

Answer

Achieving high performance on summary quality metrics

Answer

Guaranteeing the summaries are factually accurate

Question 13

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. Re-engineering features to remove proxies for protected attributes (e.g., zip codes correlated with race) directly addresses the root cause of bias without discarding all geographic information. Rebalancing the training data helps mitigate skewed representations that could amplify discriminatory patterns, preserving business value by retaining useful predictive signals while aligning with fairness principles under the AIF-C01 Responsible AI guidelines.

Answer

Remove the zip code feature from the model and retrain

Answer

Continue using the model but add a disclaimer about potential bias

Answer

Replace the model with a simpler linear model

Question 14

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. Option B is correct because AWS responsible AI guidance treats accountability as requiring identifiable human ownership: clear human oversight and defined decision-making authority ensure a person or role can be held responsible for a model's outcomes, including escalation and override paths. Option C is correct because accountability depends on traceability — detailed documentation (data provenance, design decisions, evaluation results) plus version control of models and artifacts lets auditors reproduce, explain, and attribute what a given system version did and why. Option A is wrong because full automation removes the human accountability chain rather than creating it, and consistency alone is not accountability. Option D is wrong because eliminating human review destroys oversight and can worsen unchecked bias, the opposite of responsible AI. Option E is wrong because publishing raw training data raises privacy, consent, and IP risks and is not an accountability mechanism; transparency is served through appropriate documentation and disclosures, not indiscriminate data release.

Answer

Automate all decisions to ensure consistency

Answer

Remove all human review processes to eliminate bias

Answer

Share raw training data publicly for transparency

Question 15

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 in AI systems require multiple complementary practices. (B) Implementing strong access controls (e.g., IAM policies, role-based access control) and encryption (e.g., AES-256 for data at rest, TLS 1.2+ for data in transit) protects model artifacts from unauthorized access, tampering, and exfiltration, ensuring confidentiality and integrity throughout the lifecycle. (C) Regularly testing the model against adversarial examples surfaces vulnerabilities to evasion, poisoning, and prompt-injection style attacks, allowing defenses to be hardened before real-world exploitation. (D) Monitoring model performance for data drift and concept drift detects when the model's inputs or the underlying relationships change, so degradation, unexpected behavior, or emerging security-relevant anomalies can be caught and remediated early. Together these practices cover artifact protection, adversarial resilience, and ongoing operational vigilance.

Answer

Deploy the model immediately after training without validation

Answer

Remove logging and monitoring to improve performance