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NEW QUESTION # 241
A medical company is customizing a foundation model (FM) for diagnostic purposes. The company needs the model to be transparent and explainable to meet regulatory requirements.
Which solution will meet these requirements?
Answer: B
NEW QUESTION # 242
A company is testing the security of a foundation model (FM). During testing, the company wants to get around the safety features and make harmful content.
Answer: A
Explanation:
Comprehensive and Detailed
Jailbreaking an LLM refers to deliberately crafting prompts to bypass its safety guardrails and produce restricted or harmful content.
Fuzzing is input testing for software.
DoS is a service availability attack.
Pen testing (C) is allowed with AWS authorization but doesn't describe bypassing model safeguards.
Reference:
AWS Responsible AI - Prompt Injection and Jailbreak Risks
NEW QUESTION # 243
A financial institution is using Amazon Bedrock to develop an AI application. The application is hosted in a VPC. To meet regulatory compliance standards, the VPC is not allowed access to any internet traffic.
Which AWS service or feature will meet these requirements?
Answer: D
Explanation:
AWS PrivateLink enables private connectivity between VPCs and AWS services without exposing traffic to the public internet. This feature is critical for meeting regulatory compliance standards that require isolation from public internet traffic.
* Option A (Correct): "AWS PrivateLink": This is the correct answer because it allows secure access to Amazon Bedrock and other AWS services from a VPC without internet access, ensuring compliance with regulatory standards.
* Option B: "Amazon Macie" is incorrect because it is a security service for data classification and protection, not for managing private network traffic.
* Option C: "Amazon CloudFront" is incorrect because it is a content delivery network service and does not provide private network connectivity.
* Option D: "Internet gateway" is incorrect as it enables internet access, which violates the VPC's no- internet-traffic policy.
AWS AI Practitioner References:
* AWS PrivateLink Documentation: AWS highlights PrivateLink as a solution for connecting VPCs to AWS services privately, which is essential for organizations with strict regulatory requirements.
NEW QUESTION # 244
A company's large language model (LLM) is experiencing hallucinations.
How can the company decrease hallucinations?
Answer: B
Explanation:
Hallucinations in large language models (LLMs) occur when the model generates outputs that are factually incorrect, irrelevant, or not grounded in the input data. To mitigate hallucinations, adjusting the model's inference parameters, particularly the temperature, is a well-documented approach in AWS AI Practitioner resources. The temperature parameter controls the randomness of the model's output. A lower temperature makes the model more deterministic, reducing the likelihood of generating creative but incorrect responses, which are often the cause of hallucinations.
Exact Extract from AWS AI Documents:
From the AWS documentation on Amazon Bedrock and LLMs:
"The temperature parameter controls the randomness of the generated text. Higher values (e.g., 0.8 or above) increase creativity but may lead to less coherent or factually incorrect outputs, while lower values (e.g., 0.2 or 0.3) make the output more focused and deterministic, reducing the likelihood of hallucinations." (Source: AWS Bedrock User Guide, Inference Parameters for Text Generation) Detailed Option A: Set up Agents for Amazon Bedrock to supervise the model training.Agents for Amazon Bedrock are used to automate tasks and integrate LLMs with external tools, not to supervise model training or directly address hallucinations. This option is incorrect as it does not align with the purpose of Agents in Bedrock.
Option B: Use data pre-processing and remove any data that causes hallucinations.While data pre-processing can improve model performance, identifying and removing specific data that causes hallucinations is impractical because hallucinations are often a result of the model's generative process rather than specific problematic data points. This approach is not directly supported by AWS documentation for addressing hallucinations.
Option C: Decrease the temperature inference parameter for the model.This is the correct approach. Lowering the temperature reduces the randomness in the model's output, making it more likely to stick to factual and contextually relevant responses. AWS documentation explicitly mentions adjusting inference parameters like temperature to control output quality and mitigate issues like hallucinations.
Option D: Use a foundation model (FM) that is trained to not hallucinate.No foundation model is explicitly trained to "not hallucinate," as hallucinations are an inherent challenge in LLMs. While some models may be fine-tuned for specific tasks to reduce hallucinations, this is not a standard feature of foundation models available on Amazon Bedrock.
Reference:
AWS Bedrock User Guide: Inference Parameters for Text Generation (https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters.html) AWS AI Practitioner Learning Path: Module on Large Language Models and Inference Configuration Amazon Bedrock Developer Guide: Managing Model Outputs (https://docs.aws.amazon.com/bedrock/latest/devguide/)
NEW QUESTION # 245
A company wants to collaborate with several research institutes to develop an AI model. The company needs standardized documentation of model version tracking and a record of model development.
Which solution meets these requirements?
Answer: A
Explanation:
Amazon SageMaker Model Cards provide a standardized way to document and track model information, including versions and performance. According to AWS documentation:
"SageMaker Model Cards provide a single source of truth for model information including intended use, training details, evaluation metrics, and ethical considerations to support governance and collaboration."
NEW QUESTION # 246
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