Exam AWS Certified AI Practitioner AIF-C01 topic 1 question 18 discussion

Multi-modal embedding models can process multiple types of input data, such as text and images. This allows the search application to handle queries that involve both text and images effectively.

queries that have text and images >>> Multi-modal embedding

Using multi-modal embedding to handle text and images.

The answer is A. Multi-modal embedding model. A multi-modal embedding model is a type of foundation model that can process and understand both text and images. This makes it suitable for powering a search application that handles queries containing both text and images. Here's a breakdown of the other options: B. Text embedding model: This type of model is only designed to process text data, so it wouldn't be suitable for handling image queries. C. Multi-modal generation model: This type of model is designed to generate text or images, not to search for them. D. Image generation model: This type of model is only designed to generate images, not to search for them.https://www.examtopics.com/exams/amazon/aws-certified-ai-practitioner-aif-c01/view/5/#

The correct answer is A. A multi-modal embedding model can handle both text and image queries.

A multi-modal embedding model is specifically designed to process and understand various types of data, including text and images. By converting both text and image inputs into numerical representations (embeddings), it enables the model to compare and understand the relationships between them.

Output from GenAI (Confusing / Unclear Q) :- After carefully reviewing the search results, I can see that they do not specifically address the distinction between embedding and generation models in the context of the original query. The search results primarily discuss various types of foundation models and multimodal models, but they don't directly compare embedding and generation models for the specific search application mentioned in the question. Given the lack of information directly relevant to the query in the provided search results, I cannot provide a definitive answer based on this information alone. The original question asks about using a foundation model for a search application that handles queries with text and images, but the search results don't contain specific information about embedding models for this purpose. If you'd like a more accurate answer to this question, it would be helpful to have search results that specifically discuss embedding models and generation models in the context of multimodal search applications.

The search application must handle queries that have text and images. Which type of FM should the AI practitioner use to power the search application, So, Multi Modal Embedding Model. For Result and Output, Multi-Modal Generation Model. Thus, Correct is A