Databricks Certified Generative AI Engineer - Associate

Explanation

• B. An embedding model is essential to convert query text into a vector representation, which is then used for document retrieval in the Retrieval-Augmented Generation (RAG) system. This step is critical for retrieving relevant documents based on user queries.

• D. A model signature specifies the input and output format of the deployed model, ensuring that the system can correctly process queries and return responses in the expected format. This is critical for a properly functioning deployment.

Why not the others:

• A. While a dependency management tool like conda is helpful for managing environments, it is not critical to the core functioning of the RAG model in query processing.

• C. Distributed training is beneficial for scaling and performance but not directly required for deploying a RAG model, especially if the model is already trained.

• E. A labeled training dataset is important for fine-tuning, but if you are deploying an already fine-tuned RAG model, it is not necessary for deployment and query processing.

The embedding model (B) and model signature (D) are critical to ensure that the RAG system can retrieve relevant documents and process queries correctly.