AIF-C01 Practice Test 22 — 15 Questions

Question 1

A company uses Amazon SageMaker to build a binary classification model for loan approvals. After training, the data science team wants to evaluate the model for potential bias against a protected group. Which AWS service should they use to compute bias metrics?

Accepted Answer

Amazon SageMaker Clarify. Option A is correct because SageMaker Clarify is designed specifically for bias detection and explainability. Options B, C, and D serve other purposes (monitoring, debugging, experiment tracking) and do not compute bias metrics directly.

Answer

Amazon SageMaker Model Monitor

Answer

Amazon SageMaker Debugger

Answer

Amazon SageMaker Experiments

Question 2

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.. Continuous monitoring, model cards documentation, and human-in-the-loop review are all recommended. Deploying without manual review and fully automated decisions without oversight violate responsible AI principles.

Answer

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

Answer

Use only automated decision-making without any human oversight.

Question 3

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. Option B is correct: Human-in-the-loop review for borderline cases reduces false positives. Option A is wrong: High threshold may miss hate speech. Option C is wrong: Full automation can censor legitimate speech. Option D is wrong: Averaging models may not help.

Answer

Use multiple models and average their scores

Answer

Use a single model with high confidence threshold

Answer

Rely solely on automated filtering

Question 4

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?

Accepted Answer

SHAP values. SHAP values provide per-instance (local) explanations of feature contributions. Permutation feature importance, partial dependence plots, and global feature importance all give global explanations, not local.

Answer

Permutation feature importance

Answer

Global feature importance

Answer

Partial dependence plots

Question 5

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 manages model versions and supports approval statuses (e.g., Approved, Rejected). This integrates with CI/CD pipelines to enforce review gates. Experiments, Model Monitor, and Studio do not provide such workflow capabilities.

Answer

SageMaker Studio

Answer

SageMaker Experiments

Answer

SageMaker Model Monitor

Question 6

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 can compute bias metrics on text data, and training with balanced data reduces bias. WAF is for web security, Rekognition is for image/video, CloudTrail is for auditing API calls – none are relevant to bias in NLP models.

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 7

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 DPL value of 0.15 is greater than the threshold of 0.1, indicating significant bias. Class imbalance (0.3) and label imbalance (0.4) are below their respective thresholds.

Answer

Label Imbalance

Answer

Class Imbalance

Answer

All metrics exceed thresholds

Question 8

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 policy allows sagemaker:InvokeEndpoint on all resources (*), violating the principle of least privilege. This could allow the role to invoke any SageMaker endpoint, potentially leading to unauthorized inferences. Model monitoring, training data, and data residency are not addressed by this log entry.

Answer

Insufficient training data

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Violation of data residency requirements

Answer

Absence of model monitoring

Question 9

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. The configuration creates a weekly monitoring job (cron expression for Monday) that compares endpoint predictions against a baseline and outputs bias reports to S3. It does not retrain, replace the model, or monitor training data drift.

Answer

To retrain the model weekly

Answer

To replace the model with a new version

Answer

To monitor data drift in training data

Question 10

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 correct answer is fairness because the chatbot's responses are biased and offensive, indicating a lack of fairness in treating all users appropriately. Accountability relates to ownership, privacy to data protection, and transparency to explainability.

Answer

Privacy

Answer

Transparency

Answer

Accountability

Question 11

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. Before deploying, it is critical to evaluate the training data for bias. Auditing the data for representation and fairness helps identify and mitigate bias early. Post-deployment monitoring is secondary, and simply retraining without review may perpetuate bias. Excluding demographic data might ignore important fairness dimensions.

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 12

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. Providing feature importance scores helps doctors understand why the model made a specific prediction, aligning with explainability. Removing humans oversimplifies accountability. Using a black-box model reduces explainability. Relying solely on accuracy ignores interpretability.

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 13

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. To mitigate bias, the company should measure and monitor the system's impact across demographic groups. This aligns with fairness metrics. Using more data without addressing bias may not help. Relying on human review is good but does not guarantee systematic fairness. Focusing only on performance ignores fairness.

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 14

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 requires that users know when they are interacting with AI. Disclosing that the summaries are AI-generated is fundamental. Accuracy, bias mitigation, and performance are important but do not directly address transparency about AI use.

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 15

A startup is developing a mobile app that uses facial recognition to verify user identity for account access. The app is intended for a global audience, but the training data predominantly includes images of light-skinned individuals. During beta testing, users with darker skin tones report frequent verification failures, while light-skinned users have a high success rate. The startup wants to release the app soon and needs to address this fairness issue without delaying the launch too much. The team has limited resources. Which approach should they take to most effectively mitigate the bias while meeting the launch timeline?

Accepted Answer

Collect more diverse training data and augment the existing dataset, then retrain the model. The most effective approach is to collect additional training data representing diverse skin tones and augment the dataset, then retrain the model. This directly addresses the data imbalance. Applying a post-processing rule without retraining may not fix the underlying model bias. Deferring to humans is a temporary workaround and does not scale. Reducing the threshold for all users could increase false positives and may not be acceptable.

Answer

Apply a post-processing rule to increase acceptance rate for users with darker skin tones

Answer

Lower the similarity threshold for all users to improve acceptance rates

Answer

Defer verification for users with darker skin tones to manual human review