AIF-C01 Practice Test 21 — 15 Questions

Question 1

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 incorporates adversarial examples during training, which is the most proven method to improve robustness against such attacks. Increasing model complexity, early stopping, or reducing data are not effective or may harm performance.

Answer

Reduce training data size

Answer

Use early stopping during training

Answer

Increase model complexity

Question 2

Refer to the exhibit. A team is creating an IAM policy for a SageMaker notebook user. The user needs to access training data in an S3 bucket and create models. Which responsible AI concern is most relevant to this policy?

Accepted Answer

The policy grants overly broad permissions, violating the principle of least privilege.. Option C is correct. The policy grants broad access (sagemaker:CreateModel and sagemaker:CreateNotebookInstance on all resources) without restrictions. This could allow a user to create models using any data or expose the notebook. The least privilege principle is violated, leading to potential unintended model creation or data exposure. Options A and B are less directly related; D is about explainability.

Answer

The policy does not enforce encryption for the notebook.

Answer

The policy does not restrict which S3 buckets the user can read.

Answer

The policy does not include a condition for model explainability.

Question 3

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 4

A healthcare company is training a model on sensitive patient data using Amazon SageMaker. They need to ensure that individual patient data cannot be reverse-engineered from the model. Which technique should they implement during training?

Accepted Answer

Differential privacy. Differential privacy adds noise to the training process to protect individual records. Data encryption and IAM control access but do not prevent inference from model parameters; Model Monitor is for post-deployment monitoring.

Answer

Data encryption at rest

Answer

AWS Identity and Access Management (IAM) policies

Answer

SageMaker Model Monitor

Question 5

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 involve testing for adversarial inputs, monitoring data drift, and implementing secure access controls. Using unvalidated models is risky. Removing logging reduces traceability.

Answer

Deploy the model immediately after training without validation

Answer

Remove logging and monitoring to improve performance

Question 6

Refer to the exhibit. A developer is reviewing CloudWatch Logs for a deployed model and notices the same input appears multiple times with slightly different probabilities. What responsible AI concern does this pattern suggest?

Accepted Answer

The model is not robust; it produces inconsistent predictions for the same input.. Option B is correct. Repeated identical inputs with different predictions indicate model instability (lack of robustness). This could be due to randomness in the model or adversarial conditions. Option A is irrelevant; C is possible but not the primary concern; D is about data drift, but input is same.

Answer

The model is overfitting to the training data.

Answer

The model is exhibiting bias against a demographic group.

Answer

The input data is drifting from the training distribution.

Question 7

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 8

A team is developing an AI system and wants to document key information such as intended use, performance benchmarks, and limitations. According to AWS best practices for responsible AI, what should they create?

Accepted Answer

Model cards. Model cards are a standardized format for documenting model details, promoting transparency. Other options are not specific to responsible AI documentation.

Answer

A whitepaper

Answer

A business requirement document

Answer

A technical blog

Question 9

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 10

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 11

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) enables human review loops for low-confidence predictions or sensitive cases. Other services are not designed for human-in-the-loop in conversational AI.

Answer

Amazon Rekognition

Answer

Amazon Comprehend

Answer

Amazon SageMaker Ground Truth

Question 12

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 13

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 14

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

Accepted Answer

Profitability. The core principles typically include fairness, robustness, explainability, privacy, transparency, and accountability. Profitability is a business objective, not an ethical principle of responsible AI.

Answer

Transparency

Answer

Fairness

Answer

Robustness

Question 15

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