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| Section | Objectives |
|---|---|
| Responsible AI and Security | - AI ethics and responsible use - Security, privacy, and governance in AI systems - Bias, fairness, and explainability |
| Fundamentals of Generative AI | - AWS generative AI services overview (e.g., Amazon Bedrock) - Foundation models and prompt engineering basics - Large language models (LLMs) concepts |
| Applications of Foundation Models | - Use cases for generative AI in business - Content generation and summarization - AI-powered assistants and automation |
| Fundamentals of Artificial Intelligence and Machine Learning | - Data fundamentals for AI/ML - Core AI and ML concepts - Common ML workflows and lifecycle |
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NEW QUESTION # 90
An AI company periodically evaluates its systems and processes with the help of independent software vendors (ISVs). The company needs to receive email message notifications when an ISV's compliance reports become available.
Which AWS service can the company use to meet this requirement?
Answer: B
Explanation:
AWS Data Exchange is a service that allows companies to securely exchange data with third parties, such as independent software vendors (ISVs). AWS Data Exchange can be configured to provide notifications, including email notifications, when new datasets or compliance reports become available.
Option D (Correct): "AWS Data Exchange": This is the correct answer because it enables the company to receive notifications, including email messages, when ISVs' compliance reports are available.
Option A: "AWS Audit Manager" is incorrect because it focuses on assessing an organization's own compliance, not receiving third-party compliance reports.
Option B: "AWS Artifact" is incorrect as it provides access to AWS's compliance reports, not ISVs'.
Option C: "AWS Trusted Advisor" is incorrect as it offers optimization and best practices guidance, not compliance report notifications.
AWS AI Practitioner References:
AWS Data Exchange Documentation: AWS explains how Data Exchange allows organizations to subscribe to third-party data and receive notifications when updates are available.
NEW QUESTION # 91
Which THREE of the following principles of responsible AI are most critical to this scenario? (Choose 3)
* Explainability
* Fairness
* Privacy and security
* Robustness
* Safety
Answer:
Explanation:
Explanation:
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
NEW QUESTION # 92
A fitness company has an application that uses LLMs to create new personalized exercise routines for users. The company generates the routines every week for all users in the company's database.
The company wants to reduce costs for this repetitive workload. The workload processes large volumes of requests and does not require immediate responses.
Which solution will meet these requirements?
Answer: C
Explanation:
The verified answer is C. Use batch inference with Amazon Bedrock. The workload has three decisive characteristics: it is repetitive, it processes a large volume of requests, and it does not require immediate responses. AWS documentation states that with Amazon Bedrock batch inference, users can submit multiple prompts and generate responses asynchronously. It also states that batch inference helps process a large number of requests efficiently by sending a single request and generating responses in an Amazon S3 bucket. That directly matches the weekly generation of exercise routines for all users.
AWS also explains that after the batch job is complete, the output files can be retrieved from Amazon S3, and batch inference can improve model inference performance on large datasets. This is the right architecture for offline or scheduled workloads where immediate response latency is not required.
Amazon Bedrock AgentCore is incorrect because AgentCore is for building, running, and managing agents, tools, memory, gateways, and agent workflows. The problem is not agent orchestration; it is cost-efficient high-volume offline inference.
Real-time inference with Amazon Bedrock on-demand endpoints is incorrect because real-time inference is better for interactive applications where users need immediate responses. The question explicitly says immediate responses are not required.
Real-time inference with SageMaker AI hosted endpoints is also incorrect for the same reason. Hosted endpoints are appropriate for low-latency inference requests, but this workload is scheduled and large-volume.
Therefore, Amazon Bedrock batch inference is the best solution.
NEW QUESTION # 93
A company used Amazon Bedrock to build an AI assistant. The AI assistant received a user prompt that instructed the assistant to provide the guidelines that the company used to configure the assistant. The prompt also instructed the assistant to display the full text of the initial setup. The AI assistant revealed its system prompt in the response.
Which type of prompt attack has occurred?
Answer: B
Explanation:
The attack is specifically prompt leakage because the user ' s objective is to obtain confidential system-level instructions or configuration information from the AI application. AWS defines prompt leakage as user prompts designed to "extract or reveal the system prompt, developer instructions, or other confidential configuration details." The scenario precisely matches this definition. The user asks the assistant to disclose the guidelines used to configure it and to reproduce the complete initial setup. The assistant then reveals its system prompt. The defining outcome is therefore unauthorized disclosure of protected prompt instructions.
Prompt leakage is related to the broader category of prompt attacks , and a leakage attempt can involve prompt-injection techniques. However, when AWS asks for the specific type of attack and the attacker is intentionally trying to expose the hidden system prompt or developer instructions, prompt leakage is the more precise classification.
A jailbreak generally attempts to bypass model safeguards or restrictions so the model performs behavior that would normally be prohibited. Although jailbreaks and prompt leakage can overlap, the core purpose in this scenario is disclosure of system configuration rather than simply bypassing a safety policy.
Prompt injection is the broader technique of introducing malicious instructions designed to override or manipulate existing instructions. For example, an attacker might tell the application to ignore previous instructions. AWS includes jailbreaks, prompt injections, and prompt leakages within its prompt-attack filtering capabilities.
A denied topic violation occurs when content concerns a topic explicitly configured as prohibited in Amazon Bedrock Guardrails. It does not describe system-prompt disclosure.
AWS recommends protecting generative AI applications by using Bedrock Guardrails prompt-attack detection, clearly defined system instructions, controlled access to tools and resources, input evaluation, output validation, and security testing.
Because confidential system instructions were explicitly requested and subsequently disclosed, the exact AWS-aligned classification is B. Prompt leakage .
NEW QUESTION # 94
A retail company wants to build an ML model to recommend products to customers. The company wants to build the model based on responsible practices. Which practice should the company apply when collecting data to decrease model bias?
Answer: B
Explanation:
The retail company wants to build an ML model for product recommendations using responsible practices to decrease model bias. Collecting balanced and diverse data ensures the model does not favor specific groups, reducing bias and promoting fairness, a key responsible AI practice.
Exact Extract from AWS AI Documents:
From the AWS AI Practitioner Learning Path:
"To reduce model bias, it is critical to collect balanced and diverse data that represents various demographics and user groups. This practice ensures fairness and prevents the model from disproportionately favoring certain populations." (Source: AWS AI Practitioner Learning Path, Module on Responsible AI) Detailed Explanation:
Option A: Use data from only customers who match the demography of the company's overall customer base.
Limiting data to a specific demographic may reinforce existing biases, failing to address underrepresented groups and increasing bias.
Option B: Collect data from customers who have a past purchase history.Focusing only on customers with purchase history may exclude new users, potentially introducing bias, and does not address diversity.
Option C: Ensure that the data is balanced and collected from a diverse group.This is the correct answer. A balanced and diverse dataset reduces bias by ensuring the model learns from a representative sample, aligning with responsible AI practices.
Option D: Ensure that the data is from a publicly available dataset.Public datasets may not be diverse or representative of the company's customer base and could introduce unrelated biases, failing to address fairness.
References:
AWS AI Practitioner Learning Path: Module on Responsible AI
Amazon SageMaker Developer Guide: Bias and Fairness in ML (https://docs.aws.amazon.com/sagemaker
/latest/dg/clarify-bias.html)
AWS Documentation: Responsible AI Practices (https://aws.amazon.com/machine-learning/responsible-ai/)
NEW QUESTION # 95
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