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NEW QUESTION # 274
A company wants to use its documents as a knowledge base for a large language model (LLM) in a Retrieval Augmented Generation (RAG) solution.
Which solution will meet these requirements?
Answer: A
NEW QUESTION # 275
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:
NEW QUESTION # 276
An ecommerce company is deploying a chatbot. The chatbot will give users the ability to ask questions about the company's products and receive details on users' orders. The company must implement safeguards for the chatbot to filter harmful content from the input prompts and chatbot responses.
Which AWS feature or resource meets these requirements?
Answer: B
Explanation:
The ecommerce company is deploying a chatbot that needs safeguards to filter harmful content from input prompts and responses. Amazon Bedrock Guardrails provide mechanisms to ensure responsible AI usage by filtering harmful content, such as hate speech, violence, or misinformation, making it the appropriate feature for this requirement.
Exact Extract from AWS AI Documents:
From the AWS Bedrock User Guide:
"Amazon Bedrock Guardrails enable developers to implement safeguards for generative AI applications, such as chatbots, by filtering harmful content in input prompts and model responses. Guardrails include content filters, word filters, and denied topics to ensure safe and responsible outputs." (Source: AWS Bedrock User Guide, Guardrails for Responsible AI) Detailed Option A: Amazon Bedrock GuardrailsThis is the correct answer. Amazon Bedrock Guardrails are specifically designed to filter harmful content from chatbot inputs and responses, ensuring safe interactions for users.
Option B: Amazon Bedrock AgentsAmazon Bedrock Agents are used to automate tasks and integrate with external tools, not to filter harmful content. This option does not meet the requirement.
Option C: Amazon Bedrock inference APIsAmazon Bedrock inference APIs allow users to invoke foundation models for generating responses, but they do not provide built-in content filtering mechanisms.
Option D: Amazon Bedrock custom modelsCustom models on Amazon Bedrock allow users to fine-tune models, but they do not inherently include safeguards for filtering harmful content unless explicitly implemented.
Reference:
AWS Bedrock User Guide: Guardrails for Responsible AI (https://docs.aws.amazon.com/bedrock/latest/userguide/guardrails.html) AWS AI Practitioner Learning Path: Module on Responsible AI and Model Safety Amazon Bedrock Developer Guide: Building Safe AI Applications (https://aws.amazon.com/bedrock/)
NEW QUESTION # 277
A manufacturing company uses AI to inspect products and find any damages or defects.
Which type of AI application is the company using?
Answer: C
Explanation:
The manufacturing company uses AI to inspect products for damages or defects, which involves analyzing visual data (e.g., images or videos of products). This task falls under computer vision, a type of AI application that enables machines to interpret and understand visual information, such as identifying defects in manufacturing.
Exact Extract from AWS AI Documents:
From the AWS AI Practitioner Learning Path:
"Computer vision enables machines to interpret and understand visual data from the world, such as images or videos. Common applications include defect detection in manufacturing, where AI models analyze product images to identify damages or anomalies." (Source: AWS AI Practitioner Learning Path, Module on AI Concepts) Detailed Option A: Recommendation systemRecommendation systems suggest items or actions based on user preferences (e.g., product recommendations). They are not relevant for inspecting products for defects.
Option B: Natural language processing (NLP)NLP focuses on processing and understanding text or speech, not visual data like product images. This option is incorrect.
Option C: Computer visionThis is the correct answer. Computer vision is used for tasks like defect detection in manufacturing by analyzing visual data to identify damages or defects.
Option D: Image processingWhile image processing involves manipulating images (e.g., filtering, resizing), it is a lower-level technique, not an AI application. Computer vision, which often uses image processing as a component, is the broader AI application here.
Reference:
AWS AI Practitioner Learning Path: Module on AI Concepts
Amazon Rekognition Developer Guide: Image Analysis (https://docs.aws.amazon.com/rekognition/latest/dg/what-is.html) AWS Documentation: Introduction to Computer Vision (https://aws.amazon.com/computer-vision/)
NEW QUESTION # 278
A company has a large amount of unlabeled data. The company wants to group the data based on feature similarities.
Which algorithm will meet this requirement?
Answer: B
Explanation:
Comprehensive and Detailed Explanation (AWS AI documents):
AWS machine learning fundamentals classify K-means as an unsupervised learning algorithm used to group unlabeled data into clusters based on feature similarity and distance metrics. Because the data is unlabeled and the goal is grouping rather than prediction, K-means is the most appropriate choice.
Why the other options are incorrect:
XGBoost is a supervised learning algorithm that requires labeled data.
DeepAR forecasting is designed for time series forecasting.
Linear learner is typically used for supervised regression or classification tasks.
AWS AI Study Guide Reference:
AWS unsupervised learning concepts
AWS clustering algorithms overview
NEW QUESTION # 279
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