AIF-C01 Guidelines for Responsible AI Practice Test 3 — 15 Questions

Question 1

A financial services company deploys a generative AI chatbot for customer support. They want to prevent the chatbot from generating harmful or misleading information. Which AWS service can help monitor and filter responses?

Accepted Answer

Amazon Augmented AI (A2I) with human review. Option A is correct: Amazon Augmented AI (A2I) with human review can monitor and filter responses for sensitive content. Option B is wrong: Amazon Comprehend is for understanding, not filtering. Option C is wrong: AWS WAF is for web traffic. Option D is wrong: Amazon GuardDuty is for security threats.

Answer

Amazon GuardDuty

Answer

AWS WAF

Answer

Amazon Comprehend

Question 2

A healthcare startup uses Amazon SageMaker to train a model predicting patient readmission. They need to ensure the model's predictions do not discriminate based on protected attributes like age or race. Which SageMaker feature allows them to monitor and mitigate bias during training?

Accepted Answer

SageMaker Clarify. Option B is correct: SageMaker Clarify provides bias detection and mitigation. Option A is wrong: Model Monitor is for data drift. Option C is wrong: Debugger is for debugging training issues. Option D is wrong: Autopilot automates model building.

Answer

SageMaker Model Monitor

Answer

SageMaker Autopilot

Answer

SageMaker Debugger

Question 3

A media company uses Amazon Transcribe for automatic speech recognition. They discover the model has higher error rates for non-native English speakers. Which Responsible AI principle are they failing to uphold?

Accepted Answer

Fairness. Option C is correct: Fairness requires equitable performance across groups. Option A is wrong: Explainability is about transparency. Option B is wrong: Privacy is about data protection. Option D is wrong: Robustness is about reliability under varied conditions.

Answer

Explainability

Answer

Robustness

Answer

Privacy

Question 4

An e-commerce company uses a recommendation system built with Amazon Personalize. They want to explain to customers why certain products are recommended. Which AWS service can provide model explanations?

Accepted Answer

Amazon SageMaker Clarify. Option D is correct: SageMaker Clarify can provide feature attribution for recommendations. Option A is wrong: AWS Config tracks resources. Option B is wrong: CloudTrail audits API calls. Option C is wrong: Amazon Detective investigates security findings.

Answer

AWS Config

Answer

AWS CloudTrail

Answer

Amazon Detective

Question 5

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. Option A is correct: Training with adversarial examples improves robustness. Option B is wrong: Model Monitor detects drift, not adversarial inputs. Option C is wrong: Input validation helps but not the primary defense. Option D is wrong: AWS Shield protects against DDoS.

Answer

Use SageMaker Model Monitor to detect adversarial inputs

Answer

Implement input validation and use Amazon SageMaker Shadow Testing

Answer

Apply AWS Shield Advanced

Question 6

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 7

A startup uses Amazon Lex to build a chatbot for mental health support. They must ensure user conversations are private and not used for model improvement. Which AWS service can help anonymize text data before storage?

Accepted Answer

Amazon Comprehend. Option C is correct: Amazon Comprehend can detect and redact personally identifiable information (PII). Option A is wrong: KMS encrypts but doesn't redact. Option B is wrong: Macie discovers sensitive data but doesn't anonymize. Option D is wrong: Textract extracts text from documents.

Answer

Amazon Textract

Answer

AWS Key Management Service (KMS)

Answer

Amazon Macie

Question 8

A research lab uses Amazon SageMaker to train a deep learning model for medical diagnosis. They need to ensure the model's decisions are interpretable to clinicians. Which SageMaker feature provides local and global feature importance?

Accepted Answer

SageMaker Clarify. Option D is correct: SageMaker Clarify provides SHAP-based explanations for feature importance. Option A is wrong: Debugger is for debugging training. Option B is wrong: Model Monitor is for monitoring. Option C is wrong: Experiments is for managing trials.

Answer

SageMaker Model Monitor

Answer

SageMaker Experiments

Answer

SageMaker Debugger

Question 9

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

Accepted Answer

Train the model on a representative dataset. Options A and B are correct: Diverse training data and bias detection with Clarify. Options C, D, E are not direct fairness actions.

Answer

Enable AWS CloudTrail for audit

Answer

Use a single validation set

Answer

Encrypt data at rest using AWS KMS

Question 10

Which THREE practices support transparency in AI systems? (Select three.)

Accepted Answer

Provide documentation of model limitations. Options A, B, and D are correct: documentation, user opt-out, and published performance metrics. Option C is not necessary, option E is explainability, which is related but not transparency per se.

Answer

Implement explainability methods like SHAP

Answer

Use open-source models exclusively

Question 11

Which TWO actions can help mitigate bias in a face recognition model trained on AWS? (Select two.)

Accepted Answer

Ensure the training dataset is balanced across demographics. Options A and C are correct: balanced dataset and subgroup evaluation. Option B is for content moderation, D is network size irrelevant, E is deployment not mitigation.

Answer

Deploy the model in multiple regions

Answer

Use a larger neural network

Answer

Use Amazon Rekognition's content moderation

Question 12

Refer to the exhibit. An AWS customer runs SageMaker Clarify to evaluate bias in their training data. The report shows multiple metrics with status 'violated'. What should the customer do next?

Accepted Answer

Use data augmentation to balance the dataset. Option B is correct: Data augmentation or resampling can address class imbalance and demographic parity issues. Option A is wrong: Simply retraining with more data may not fix imbalance. Option C is wrong: Ignoring violations is irresponsible. Option D is wrong: Reducing features may not help.

Answer

Reduce the number of features

Answer

Retrain the model with more data

Answer

Ignore the metrics because thresholds are too strict

Question 13

Refer to the exhibit. A team is configuring a SageMaker Model Bias job. The baseline job has been completed. However, the bias job fails with a resource not found error. What is the most likely cause?

Accepted Answer

The BaseliningJobName is incorrect. Option C is correct: The BaseliningJobName must exactly match the name of the completed baseline job. Option A is wrong: IAM permissions would give a different error. Option B is wrong: ml.m5.large is supported. Option D is wrong: Runtime is sufficient.

Answer

The StoppingCondition is too short

Answer

The instance type ml.m5.large is not supported

Answer

The IAM role lacks permissions to DescribeBaselineJob

Question 14

Refer to the exhibit. An ML team finds that their training data is stored in two subfolders under s3://my-bucket/train/. They need to ensure that the dataset is balanced for training a classification model. What should they do?

Accepted Answer

Count the number of files in each subfolder and resample. Option B is correct: They need to check the number of files in each subfolder and resample if necessary. Option A is wrong: AWS Glue can be used but not specifically for balancing. Option C is wrong: Rekognition custom labels are for image labeling. Option D is wrong: Versioning does not affect balance.

Answer

Use AWS Glue to create a balanced dataset

Answer

Use Amazon Rekognition custom labels

Answer

Enable versioning on the bucket

Question 15

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