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NEW QUESTION # 283
A research group wants to test different generative AI models to create research papers. The research group has defined a prompt and needs a method to assess the models' output. The research group wants to use a team of scientists to perform the output assessments.
Which solution will meet these requirements?
Answer: B
Explanation:
The correct answer is C because Amazon Bedrock's model evaluation feature allows users to compare outputs from different foundation models using human evaluation or automatic metrics. It enables the creation of structured evaluations where human reviewers (in this case, scientists) can assess model responses based on custom criteria like relevance, coherence, or accuracy.
From AWS documentation:
"Amazon Bedrock provides model evaluation capabilities that support both automatic and human evaluation.
You can define custom evaluation prompts and collect assessments from reviewers to compare foundation model outputs for tasks such as summarization, text generation, and more." This solution is ideal for research workflows requiring domain experts to provide feedback on LLM-generated content.
Explanation of other options:
A). Amazon Personalize is used for building recommendation systems, not for evaluating model output.
B). Amazon Rekognition is used for analyzing images and videos (e.g., moderation, facial recognition), not textual output.
D). Amazon Comprehend provides NLP services like sentiment analysis, but sentiment is not sufficient for full quality evaluation of research paper generation.
Referenced AWS AI/ML Documents and Study Guides:
* Amazon Bedrock Developer Guide - Model Evaluation Overview
* AWS Generative AI Best Practices
* AWS ML Specialty Study Guide - Evaluation and Feedback Loops in LLMs
NEW QUESTION # 284
A law firm wants to build an AI application by using large language models (LLMs). The application will read legal documents and extract key points from the documents.
Which solution meets these requirements?
Answer: C
NEW QUESTION # 285
An ecommerce company is using a chatbot to automate the customer order submission process. The chatbot is powered by AI and Is available to customers directly from the company's website 24 hours a day, 7 days a week.
Which option is an AI system input vulnerability that the company needs to resolve before the chatbot is made available?
Answer: D
Explanation:
The ecommerce company's chatbot, powered by AI, automates customer order submissions and is accessible 24/7 via the website. Prompt injection is an AI system input vulnerability where malicious users craft inputs to manipulate the chatbot's behavior, such as bypassing safeguards or accessing unauthorized information. This vulnerability must be resolved before the chatbot is made available to ensure security.
Exact Extract from AWS AI Documents:
From the AWS Bedrock User Guide:
"Prompt injection is a vulnerability in AI systems, particularly chatbots, where malicious inputs can manipulate the model's behavior, potentially leading to unauthorized actions or harmful outputs. Implementing guardrails and input validation can mitigate this risk." (Source: AWS Bedrock User Guide, Security Best Practices) Detailed Option A: Data leakageData leakage refers to the unintended exposure of sensitive data during model training or inference, not an input vulnerability affecting a chatbot's operation.
Option B: Prompt injectionThis is the correct answer. Prompt injection is a critical input vulnerability for chatbots, where malicious prompts can exploit the AI to produce harmful or unauthorized responses, a risk that must be addressed before launch.
Option C: Large language model (LLM) hallucinationsLLM hallucinations refer to the model generating incorrect or ungrounded responses, which is an output issue, not an input vulnerability.
Option D: Concept driftConcept drift occurs when the data distribution changes over time, affecting model performance. It is not an input vulnerability but a long-term performance issue.
Reference:
AWS Bedrock User Guide: Security Best Practices (https://docs.aws.amazon.com/bedrock/latest/userguide/security.html) AWS AI Practitioner Learning Path: Module on AI Security and Vulnerabilities AWS Documentation: Securing AI Systems (https://aws.amazon.com/security/)
NEW QUESTION # 286
A company uses Amazon SageMaker for its ML pipeline in a production environment. The company has large input data sizes up to 1 GB and processing times up to 1 hour. The company needs near real-time latency.
Which SageMaker inference option meets these requirements?
Answer: D
Explanation:
Real-time inference is designed to provide immediate, low-latency predictions, which is necessary when the company requires near real-time latency for its ML models. This option is optimal when there is a need for fast responses, even with large input data sizes and substantial processing times.
* Option A (Correct): "Real-time inference": This is the correct answer because it supports low- latency requirements, which are essential for real-time applications where quick response times are needed.
* Option B: "Serverless inference" is incorrect because it is more suited for intermittent, small-scale inference workloads, not for continuous, large-scale, low-latency needs.
* Option C: "Asynchronous inference" is incorrect because it is used for workloads that do not require immediate responses.
* Option D: "Batch transform" is incorrect as it is intended for offline, large-batch processing where immediate response is not necessary.
AWS AI Practitioner References:
* Amazon SageMaker Inference Options: AWS documentation describes real-time inference as the best solution for applications that require immediate prediction results with low latency.
NEW QUESTION # 287
An AI practitioner wants to use a foundation model (FM) to design a search application. The search application must handle queries that have text and images.
Which type of FM should the AI practitioner use to power the search application?
Answer: A
NEW QUESTION # 288
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