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NEW QUESTION # 150
A company wants to develop a solution that uses generative AI to create content for product advertisements, Including sample images and slogans.
Select the correct model type from the following list for each action. Each model type should be selected one time. (Select THREE.)
* Diffusion model
* Object detection model
* Transformer-based model
Answer:
Explanation:
Explanation:
Diffusion models are state-of-the-art generative models for creating high-quality, realistic images from textual prompts or other forms of conditioning. These are the foundational technology behind tools like Amazon Bedrock Titan Image Generator and other generative image models.
Reference: AWS Generative AI Overview, Diffusion Models Explained - AWS Blog Transformer-based models (such as GPT or Amazon Titan Text) are designed for generating and understanding natural language. These models can generate coherent, contextually relevant slogans based on product information.
Reference: AWS Generative AI on Bedrock, Transformers Explained - AWS
Object detection models are designed to identify and locate objects within images, which makes them suitable for verifying that specific brand elements (like logos or products) are correctly positioned in the generated content.
Reference: AWS Rekognition Object Detection, Object Detection Overview - AWS
NEW QUESTION # 151
What are tokens in the context of generative AI models?
Answer: C
Explanation:
Tokens in generative AI models are the smallest units that the model processes, typically representing words, subwords, or characters. They are essential for the model to understand and generate language, breaking down text into manageable parts for processing.
* Option A (Correct): "Tokens are the basic units of input and output that a generative AI model operates on, representing words, subwords, or other linguistic units": This is the correct definition of tokens in the context of generative AI models.
* Option B: "Mathematical representations of words" describes embeddings, not tokens.
* Option C: "Pre-trained weights of a model" refers to the parameters of a model, not tokens.
* Option D: "Prompts or instructions given to a model" refers to the queries or commands provided to a model, not tokens.
AWS AI Practitioner References:
* Understanding Tokens in NLP: AWS provides detailed explanations of how tokens are used in natural language processing tasks by AI models, such as in Amazon Comprehend and other AWS AI services.
NEW QUESTION # 152
What is tokenization used for in natural language processing (NLP)?
Answer: B
Explanation:
The verified answer is C. To break text into smaller units for processing. AWS defines tokenization as the process of breaking input text into smaller units called tokens. These tokens can be words, subwords, or characters, and tokenization is used as a preprocessing step for natural language processing tasks. This exactly matches option C.
Tokenization is fundamental because machine learning and generative AI systems do not process raw human language in the same way humans read sentences. Text must first be split into units the model can represent, count, embed, and process mathematically. For example, a sentence might be split into words, subword pieces, punctuation, or characters depending on the tokenizer and model architecture. These tokens can then be converted into numerical representations for downstream tasks such as classification, summarization, question answering, translation, or text generation.
Option A is incorrect because encryption protects data confidentiality by transforming readable text into unreadable ciphertext. Tokenization in NLP is not encryption. The original purpose is linguistic and computational processing, not data protection.
Option B is incorrect because compression reduces file size. Tokenization may reduce text into units, but its purpose is not storage compression. It prepares text for analysis by NLP systems.
Option D is incorrect because translation is a downstream NLP task that converts text from one language to another. Tokenization may be used before translation, but it is not itself translation.
The wording "break text into smaller units" is the defining phrase. Therefore, the correct answer is C.
NEW QUESTION # 153
A company wants to implement a large language model (LLM)-based chatbot to provide customer service agents with real-time contextual responses to customers' inquiries. The company will use the company's policies as the knowledge base.
Answer: C
Explanation:
Comprehensive and Detailed
Retraining or pre-training is costly and unnecessary for just using company policies.
Fine-tuning adapts models but is inefficient for frequently changing company documents.
Retrieval-Augmented Generation (RAG) is the best approach - it retrieves relevant policy documents from a knowledge base and feeds them into the model context in real time, ensuring accurate and up-to-date responses.
Reference:
AWS Documentation - RAG with Amazon Bedrock
NEW QUESTION # 154
A company wants to improve the accuracy of the responses from a generative AI application. The application uses a foundation model (FM) on Amazon Bedrock.
Which solution meets these requirements MOST cost-effectively?
Answer: A
Explanation:
The company wants to improve the accuracy of a generative AI application using a foundation model (FM) on Amazon Bedrock in the most cost-effective way. Prompt engineering involves optimizing the input prompts to guide the FM to produce more accurate responses without modifying the model itself. This approach is cost-effective because it does not require additional computational resources or training, unlike fine-tuning or retraining.
Exact Extract from AWS AI Documents:
From the AWS Bedrock User Guide:
"Prompt engineering is a cost-effective technique to improve the performance of foundation models. By crafting precise and context-rich prompts, users can guide the model to generate more accurate and relevant responses without the need for fine-tuning or retraining." (Source: AWS Bedrock User Guide, Prompt Engineering for Foundation Models) Detailed Option A: Fine-tune the FM.Fine-tuning involves retraining the FM on a custom dataset, which requirescomputational resources, time, and cost (e.g., for Amazon Bedrock fine-tuning jobs). It is not the most cost-effective solution.
Option B: Retrain the FM.Retraining an FM from scratch is highly resource-intensive and expensive, as it requires large datasets and significant compute power. This is not cost-effective.
Option C: Train a new FM.Training a new FM is the most expensive option, as it involves building a model from the ground up, requiring extensive data, compute resources, and expertise. This is not cost-effective.
Option D: Use prompt engineering.This is the correct answer. Prompt engineering adjusts the input prompts to improve the FM's responses without incurring additional compute costs, making it the most cost-effective solution for improving accuracy on Amazon Bedrock.
Reference:
AWS Bedrock User Guide: Prompt Engineering for Foundation Models (https://docs.aws.amazon.com/bedrock/latest/userguide/prompt-engineering.html) AWS AI Practitioner Learning Path: Module on Generative AI Optimization Amazon Bedrock Developer Guide: Cost Optimization for Generative AI (https://aws.amazon.com/bedrock/)
NEW QUESTION # 155
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