An AI practitioner wants to use a foundation model (FM) to design a search application. 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?
I remember studying that text embedding models are great for text-only tasks, so they probably wouldn't work here. A seems like the best fit, but I'm still a bit unsure.
I'm not entirely sure, but I remember a practice question where we discussed how text and image data need to be processed together, which makes me lean towards A as well.
I'm pretty confident that the answer is A, a multi-modal embedding model. This type of model is specifically designed to work with both text and images, which is exactly what the search application requires.
Okay, let's see. The key here is that the search application needs to handle both text and images. So I believe a multi-modal embedding model would be the most appropriate choice to capture the relationships between the text and visual information.
Hmm, I'm a bit unsure about this one. I'm not entirely familiar with the different types of foundation models and their capabilities. I'll need to think this through carefully.
Okay, let's see. The key server downloads encryption keys and maintains encryption policies - those seem like the two main purposes based on the options provided.
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