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NEW QUESTION # 263
Which option is a use case for generative AI models?
Answer: C
Explanation:
Generative AI models are used to create new content based on existing data. One common use case is generating photorealistic images from text descriptions, which is particularly useful in digital marketing, where visual content is key to engaging potential customers.
Option B (Correct): "Creating photorealistic images from text descriptions for digital marketing": This is the correct answer because generative AI models, like those offered by Amazon Bedrock, can create images based on text descriptions, making them highly valuable for generating marketing materials.
Option A: "Improving network security by using intrusion detection systems" is incorrect because this is a use case for traditional machine learning models, not generative AI.
Option C: "Enhancing database performance by using optimized indexing" is incorrect as it is unrelated to generative AI.
Option D: "Analyzing financial data to forecast stock market trends" is incorrect because it typically involves predictive modeling rather than generative AI.
AWS AI Practitioner References:
Use Cases for Generative AI Models on AWS: AWS highlights the use of generative AI for creative content generation, including image creation, text generation, and more, which is suited for digital marketing applications.
NEW QUESTION # 264
A company has a foundation model (FM) that was customized by using Amazon Bedrock to answer customer queries about products. The company wants to validate the model's responses to new types of queries. The company needs to upload a new dataset that Amazon Bedrock can use for validation.
Which AWS service meets these requirements?
Answer: D
Explanation:
I'll continue to format the remaining questions in the same format. Stay tuned!
NEW QUESTION # 265
An AI practitioner needs to improve the accuracy of a natural language generation model. The model uses rapidly changing inventory data.
Which technique will improve the model's accuracy?
Answer: A
Explanation:
The requirement is to improve the accuracy of a natural language generation (NLG) model that relies on rapidly changing inventory data. Let's evaluate the options:
* A. Transfer learning: This involves pre-training a model on a large dataset and fine-tuning it for a specific task. While effective for general model improvement, it does not specifically address the challenge of incorporating rapidly changing inventory data into the model's responses.
* B. Federated learning: This technique trains models across decentralized devices while keeping data localized, primarily for privacy purposes. It is not designed to handle rapidly changing data or improve NLG model accuracy in this context.
* C. Retrieval Augmented Generation (RAG): RAG combines a language model with a retrieval mechanism that fetches relevant, up-to-date information (e.g., inventory data) from an external source during inference. This is ideal for scenarios with dynamic data, as it ensures the model's responses are grounded in the latest information, improving accuracy.
* D. One-shot prompting: This involves providing a single example to guide the model's output. While useful for specific tasks, it does not scale well for rapidly changing data or ensure consistent accuracy with dynamic inventory updates.
Exact Extract Reference: According to AWS documentation on generative AI techniques, "Retrieval Augmented Generation (RAG) enhances large language models by retrieving relevant documents or data at inference time, enabling the model to generate accurate and contextually relevant responses, especially for dynamic or frequently updated datasets." (Source: AWS Generative AI Glossary, https://aws.amazon.com
/what-is/retrieval-augmented-generation/). This directly addresses the need for accuracy with rapidly changing inventory data.
RAG is the most suitable technique for this scenario, as it allows the model to access and incorporate the latest inventory data, making C the correct answer.
:
AWS Generative AI Glossary: Retrieval Augmented Generation (https://aws.amazon.com/what-is/retrieval- augmented-generation/) AWS Bedrock Documentation (contextual use of RAG in LLMs) AWS AI Practitioner Study Guide (focus on generative AI techniques for dynamic data)
NEW QUESTION # 266
A company needs to train an ML model to classify images of different types of animals. The company has a large dataset of labeled images and will not label more data. Which type of learning should the company use to train the model?
Answer: D
Explanation:
Supervised learning is appropriate when the dataset is labeled. The model uses this data to learn patterns and classify images. Unsupervised learning, reinforcement learning, and active learning are not suitable since they either require unlabeled data or different problem settings. References: AWS Machine Learning Best Practices.
NEW QUESTION # 267
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
Answer:
Explanation:
NEW QUESTION # 268
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