Exam Certified Generative AI Engineer Associate topic 1 question 5 discussion

Answer C, B. Foundation Model APIs Description: An API that accesses the LLM model, allowing you to use the model's features, but is not suitable for collecting real-time sports data directly. C.Feature Serving Description: Feature Serving is a function that provides features that a machine learning model can use in real time. This allows real-time sports data (e.g. scores, statistics) to be fed into the model, making it ideal for generating analytics based on this data.

I am changing my vote. I think C. Feature Serving is the correct answer. In this post from microsoft, it talks about what is feature serving. https://learn.microsoft.com/en-us/azure/databricks/machine-learning/feature-store/feature-function-serving With Databricks Feature Serving, you can serve structured data for retrieval augmented generation (RAG) applications, as well as features that are required for other applications, such as models served outside of Databricks or any other application that requires features based on data in Unity Catalog. The code provided in https://learn.microsoft.com/en-us/azure/databricks/machine-learning/feature-store/feature-serving-tutorial gives an example of how to use feature serving.

I vote for B. Foundation Model API.