Full Access Amazon Web Services AIF-C01 Tutorials

This question maps responsible AI principles to specific AI system practices as defined in AWS Responsible AI Guidelines and Amazon Bedrock Responsible AI documentation.

Scenario 1:

Encrypt the application data, and isolate the application on a private network.

→ Principle: Privacy and Security

From AWS documentation:

"Protecting user data through encryption, secure network isolation, and access control aligns with the Responsible AI principle of privacy and security. AWS recommends securing all data used by AI systems, both in transit and at rest, to maintain trust and regulatory compliance."

Scenario 2:

Evaluate how different population groups will be impacted.

→ Principle: Fairness

From AWS documentation:

"The fairness principle ensures that AI models do not discriminate or generate biased outcomes across population groups. Fairness assessment involves evaluating performance metrics across demographic segments and mitigating any bias detected."

Scenario 3:

Test the application with unexpected data to ensure the application will work in unique situations.

→ Principle: Robustness

From AWS documentation:

"Robustness refers to an AI system’s ability to maintain reliable performance under varied, noisy, or unexpected input conditions. Testing for robustness helps ensure the model generalizes well and behaves safely in edge cases."

Referenced AWS AI/ML Documents and Study Guides:

AWS Responsible AI Practices Whitepaper – Core Principles of Responsible AI

Amazon Bedrock Documentation – Responsible AI and Safety Controls

AWS Certified Machine Learning Specialty Guide – AI Governance and Model Evaluation

The correct answer is D because autoencoders are an unsupervised machine learning method commonly used for anomaly detection when labeled data is not available.

From AWS documentation:

"Autoencoders learn to compress and reconstruct input data. During anomaly detection, they learn normal patterns in data. Data points that the model cannot accurately reconstruct are flagged as anomalies."

This approach is ideal when there is no labeled data and when patterns must be learned based on normal behavior alone — a common situation in IoT sensor data environments.

Explanation of other options:

A. Linear regression requires labeled data and is used for predicting continuous values.

B. Classification requires labeled data to assign inputs into categories.

C. Decision trees are supervised learning models and also require labeled datasets.

Referenced AWS AI/ML Documents and Study Guides:

AWS Machine Learning Specialty Guide – Unsupervised Learning Techniques

Amazon SageMaker Examples – Anomaly Detection Using Autoencoders

AWS CloudTrail is a service that enables governance, compliance, and operational and risk auditing of your AWS account. It tracks API calls and identifies unauthorized access attempts to AWS resources, including Amazon Bedrock.

AWS CloudTrail:

Provides detailed logs of all API calls made within an AWS account, including those to Amazon Bedrock.

Can identify unauthorized access attempts by logging and monitoring the API calls, which helps in setting appropriate IAM policies and roles.

Why Option B is Correct:

Monitoring and Security: CloudTrail logs all access requests and helps detect unauthorized access attempts.

Auditing and Compliance: The logs can be used to audit user activity and enforce security measures.

Why Other Options are Incorrect:

A. AWS Audit Manager: Used for automating audit preparation, not for tracking real-time unauthorized access attempts.

C. Amazon Fraud Detector: Designed to detect fraudulent online activities, not unauthorized access to AWS services.

D. AWS Trusted Advisor: Provides best practice recommendations for AWS resources, not access monitoring.

Thus, B is the correct answer for identifying unauthorized users attempting to access Amazon Bedrock.

Providing examples of text passages with corresponding positive or negative labels in the prompt followed by the new text passage to be classified is the correct prompt engineering strategy for using a large language model (LLM) on Amazon Bedrock for sentiment analysis.

Example-Driven Prompts:

This strategy, known as few-shot learning, involves giving the model examples of input-output pairs (e.g., text passages with their sentiment labels) to help it understand the task context.

It allows the model to learn from these examples and apply the learned pattern to classify new text passages correctly.

Why Option A is Correct:

Guides the Model: Providing labeled examples teaches the model how to perform sentiment analysis effectively, increasing accuracy.

Contextual Relevance: Aligns the model's responses to the specific task of classifying sentiment.

Why Other Options are Incorrect:

B. Detailed explanation of sentiment analysis: Unnecessary for the model's operation; it requires examples, not explanations.

C. New text passage without context: Provides no guidance or learning context for the model.

D. Unrelated task examples: Would confuse the model and lead to inaccurate results.