What is the primary goal of a Retrieval-Augmented Generation (RAG) system?

Which two main components form the core of a RAG pipeline?

In Azure, which service is most commonly used for document retrieval in a RAG implementation?

Why is document chunking important in RAG implementations?

What is a key advantage of using hybrid search (keyword + vector) in RAG systems?

You are building a chatbot using Azure OpenAI and Azure AI Search. How can you ensure the model uses your enterprise data accurately?

What is one effective strategy to reduce hallucination in RAG systems?

In Azure, what can be used to generate embeddings for documents in a RAG pipeline?

You notice your RAG system retrieves too many irrelevant documents. Which adjustment can help improve retrieval quality?

Which metric would best evaluate retrieval performance in a RAG pipeline?

What happens during the “retrieval” step of a RAG pipeline?

What is the purpose of embedding text in a RAG architecture?

Which Azure service can be used as a vector store for storing embeddings?

What is the main role of the LLM in a RAG pipeline?

In Azure OpenAI, how is RAG different from fine-tuning a model?

What is one disadvantage of using overly large document chunks in RAG?

Which step ensures that retrieved results are relevant and ranked properly?

When designing a RAG system, why might you use Azure Cognitive Services for Language?

How can RAG help organizations protect sensitive data when using LLMs?

In Azure AI Search, which feature allows you to perform semantic ranking in RAG?

What is the typical sequence of operations in a RAG workflow?

How can you reduce latency in a RAG application using Azure AI Search?

What is the best approach to test whether your RAG system retrieves relevant context?

You are integrating Azure OpenAI with a custom FAQ dataset. What should you do first in a RAG setup?

What can happen if the retrieved passages are unrelated to the query?

What is the primary difference between “retrieval-augmented” and “knowledge-grounded” generation?

What should be monitored to ensure consistent performance of a RAG system in production?

In Azure, which service provides capabilities to build and manage AI Agents with tools and memory?

What is the key difference between a traditional chatbot and an AI agent?

What component provides short-term and long-term memory for an Azure AI Agent?