Which task describes a use case for intelligent document processing (IDP)?
Answer(s): D
Intelligent document processing is designed to extract, classify, and structure data from unstructured documents such as scanned files, PDFs, and images using OCR and AI, enabling automatic data extraction and formatting for downstream systems.
A company is using supervised learning to train an AI model on a small labeled dataset that is specific to a target task.Which step of the foundation model (FM) lifecycle does this describe?
Answer(s): A
Fine-tuning is the process of training a pre-existing foundation model on a smaller, labeled dataset that is specific to a target task, allowing the model to adapt to the company's requirements.
A design company is using a foundation model (FM) on Amazon Bedrock to generate images for various projects. The company wants to have control over how detailed or abstract each generated image appears.Which model parameter should the company modify?
Answer(s): C
The “generation step” parameter controls how detailed or abstract an image appears during generation. By adjusting the number of generation steps, you can influence the level of refinement and detail in the output -more steps yield more detailed images, while fewer steps result in more abstract images.
Which AWS service makes foundation models (FMs) available to help users build and scale generative AIapplications?
Answer(s): B
Amazon Bedrock is an AWS service that allows users to build and scale generative AI applications using foundation models (FMs) without the need to manage infrastructure. It provides access to pre-trained models from AWS and third-party providers, enabling developers to integrate generative AI capabilities like text generation, summarization, and question-answering into their applications. This makes Amazon Bedrock the ideal choice for building and scaling generative AI solutions.
HOTSPOT (Drag and Drop is not supported)A company has developed a large language model (LLM) and wants to make the LLM available to multiple internal teams. The company needs to select the appropriate inference mode for each team.Select the correct inference mode from the following list for each use case. Each inference mode should be selected one or more times.Hot Area:
The company’s chatbot needs predictions from the LLM to understand users’ intent with minimal latency - Real-time inferenceThis scenario requires Real-time inference, as chatbots typically need immediate responses with low latency to provide an interactive user experience.A data processing job needs to query the LLM to process gigabytes of text files on weekends - Batch transformThis use case is best suited for Batch transform. Since the data processing job involves handling large amounts of data, and it is scheduled for weekends, batch processing can handle these large volumes efficiently.The company’s engineering team needs to create an API that can process small pieces of text content and provide low-latency predictions - Real-time inferenceReal-time inference is the correct choice here, as the API needs to process text quickly and provide immediate responses, making real-time inference appropriate.
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