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158. Frage
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?
Antwort: C
Begründung:
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.
159. Frage
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?
Antwort: D
Begründung:
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.
160. Frage
An AI practitioner has a database of animal photos. The AI practitioner wants to automatically identify and categorize the animals in the photos without manual human effort.
Which strategy meets these requirements?
Antwort: D
Begründung:
Object detection is the correct strategy for automatically identifying and categorizing animals in photos.
* Object Detection:
* A computer vision technique that identifies and locates objects within an image and assigns them to predefined categories.
* Ideal for tasks such as identifying animals in photos, where the goal is to detect specific objects (animals) and categorize them accordingly.
* Why Option A is Correct:
* Automatic Identification: Object detection models can automatically identify different types of animals in the images without manual intervention.
* Categorization Capability: Assigns labels to detected objects, fulfilling the requirement for categorizing animals.
* Why Other Options are Incorrect:
* B. Anomaly detection: Identifies outliers or unusual patterns, not specific objects in images.
* C. Named entity recognition: Used in NLP to identify entities in text, not for image processing.
* D. Inpainting: Used for filling in missing parts of images, not for detecting or categorizing objects.
161. Frage
An airline company wants to build a conversational AI assistant to answer customer questions about flight schedules, booking, and payments. The company wants to use large language models (LLMs) and a knowledge base to create a text-based chatbot interface.
Which solution will meet these requirements with the LEAST development effort?
Antwort: D
Begründung:
The airline company aims to build a conversational AI assistant using large language models (LLMs) and a knowledge base to create a text-based chatbot with minimal development effort. Retrieval Augmented Generation (RAG) on Amazon Bedrock is an ideal solution because it combines LLMs with a knowledge base to provide accurate, contextually relevant responses without requiring extensive model training or custom development. RAG retrieves relevant information from a knowledge base and uses an LLM to generate responses, simplifying the development process.
Exact Extract from AWS AI Documents:
From the AWS Bedrock User Guide:
"Retrieval Augmented Generation (RAG) in Amazon Bedrock enables developers to build conversational AI applications by combining foundation models with external knowledge bases. This approach minimizes development effort by leveraging pre-trained models and integrating them with data sources, such as FAQs or databases, to provide accurate and contextually relevant responses." (Source: AWS Bedrock User Guide, Retrieval Augmented Generation) Detailed Explanation:
Option A: Train models on Amazon SageMaker Autopilot.SageMaker Autopilot is designed for automated machine learning (AutoML) tasks like classification or regression, not for building conversational AI with LLMs and knowledge bases. It requires significant data preparation and is not optimized for chatbot development, making it less suitable.
Option B: Develop a Retrieval Augmented Generation (RAG) agent by using Amazon Bedrock.This is the correct answer. RAG on Amazon Bedrock allows the company to use pre-trained LLMs and integrate them with a knowledge base (e.g., flight schedules or FAQs) to build a chatbot with minimal effort. It avoids the need for extensive training or coding, aligning with the requirement for least development effort.
Option C: Create a Python application by using Amazon Q Developer.While Amazon Q Developer can assist with code generation, building a chatbot from scratch in Python requires significant development effort, including integrating LLMs and a knowledge base manually, which is more complex than using RAG on Bedrock.
Option D: Fine-tune models on Amazon SageMaker Jumpstart.Fine-tuning models on SageMaker Jumpstart requires preparing training data and customizing LLMs, which involves more effort than using a pre-built RAG solution on Bedrock. This option is not the least effort-intensive.
References:
AWS Bedrock User Guide: Retrieval Augmented Generation (https://docs.aws.amazon.com/bedrock/latest
/userguide/rag.html)
AWS AI Practitioner Learning Path: Module on Generative AI and Conversational AI Amazon Bedrock Developer Guide: Building Conversational AI (https://aws.amazon.com/bedrock/)
162. Frage
A company is using Amazon SageMaker to develop AI models.
Select the correct SageMaker feature or resource from the following list for each step in the AI model lifecycle workflow. Each SageMaker feature or resource should be selected one time or not at all. (Select TWO.) SageMaker Clarify SageMaker Model Registry SageMaker Serverless Inference
Antwort:
Begründung:
Reference:
AWS SageMaker Documentation: Model Registry (https://docs.aws.amazon.com/sagemaker/latest/dg/model-registry.html) AWS SageMaker Documentation: Serverless Inference (https://docs.aws.amazon.com/sagemaker/latest/dg/serverless-inference.html) AWS AI Practitioner Study Guide (conceptual alignment with SageMaker features for model lifecycle management and inference) Let's format this question according to the specified structure and provide a detailed, verified answer based on AWS AI Practitioner knowledge and official AWS documentation. The question focuses on selecting an AWS database service that supports storage and queries of embeddings as vectors, which is relevant to generative AI applications.
163. Frage
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