AWS Certified Cloud Practitioner

Explanation

Why A is correct:

• A multi-modal embedding model is specifically designed to handle multiple types of data (text, images, audio, etc.) and convert them into vector embeddings in a shared semantic space.
• For a search application that needs to handle both text and image queries, a multi-modal embedding model can create embeddings for both modalities that are comparable, enabling cross-modal search capabilities.
• This allows users to search with text and find relevant images, or search with images and find relevant text content.

Why other options are incorrect:

• B. Text embedding model: Only handles text data and cannot process or understand image content.
• C. Multi-modal generation model: While it can handle multiple modalities, it's designed for generation tasks (creating content) rather than search/retrieval tasks.
• D. Image generation model: Only handles image generation and cannot process text queries or create comparable embeddings for search.

Key Concept: Multi-modal embedding models create vector representations of different data types in a shared space, enabling cross-modal similarity search and retrieval.