AWS Certified AI Practitioner Practice Exam

An AI practitioner wants to design a search application that handles text and images. Which type of foundation model should they use?

1. Multi-modal embedding model

2. Text embedding model

3. Multi-modal generation model

4. Image generation model

The correct answer is: Multi-modal embedding model

To design a search application that effectively handles both text and images, a multi-modal embedding model is the most appropriate choice. This type of foundation model is specifically designed to process and understand multiple types of data inputs, including both text and images. A multi-modal embedding model works by encoding various forms of data into a shared space where semantic relationships can be established regardless of the data type. As a result, the model can efficiently retrieve, compare, and analyze images in conjunction with relevant text. This capability is crucial for a search application, where users often seek information by providing image inputs or text queries. In contrast, the other options are limited to one type of data. Text embedding models focus solely on text data and would not be able to process images, making them unsuitable for a search application that requires handling both forms of content. Image generation models, while they excel in creating images, do not facilitate searching for or integrating textual information. Multi-modal generation models are designed for generating output across different modalities but do not inherently provide the embedding capabilities necessary for effective searching. Choosing a multi-modal embedding model thus ensures that the search application can comprehensively and efficiently manage the complexities of both text and image data, allowing for a more versatile and accurate user experience.