AIF-C01 Guidelines for Responsible AI Practice Test 4 — 15 Questions

Question 1

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 2

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 3

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 4

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 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

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 7

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 8

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 can compute SHAP values for per-prediction explanations. SageMaker Pipelines can orchestrate the generation of reports on a schedule. Data Wrangler is for data preparation, Model Monitor for drift, Ground Truth for labeling – none generate per-prediction explanation reports automatically.

Answer

SageMaker Model Monitor + Amazon QuickSight

Answer

SageMaker Ground Truth + AWS Lambda

Answer

SageMaker Data Wrangler + SageMaker Studio

Question 9

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.. Regularly auditing predictions for disparate impact and applying adversarial debiasing are proven techniques. Simply removing attributes may not eliminate bias due to correlated proxies. Balancing datasets is helpful but not sufficient alone. Complex models do not guarantee fairness.

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 10

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 11

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 12

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 13

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

Answer

Violation of data residency requirements

Answer

Absence of model monitoring

Question 14

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 15

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