Generative AI Leader Fundamentals of Generative AI Practice Question
Which THREE components are core to a typical Retrieval Augmented Generation (RAG) system?
⚠ Common exam trap
Google Cloud often tests the distinction between core mandatory components (embedding model, vector DB, LLM) and optional auxiliary components (classifier, rewriter, reranker) to see if candidates understand the minimal viable RAG architecture versus extended pipelines.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Vector database
In a typical RAG system, the vector database (B) is core because it stores embedded representations of documents and enables efficient similarity search to retrieve relevant context for a query. The embedding model (C) is also essential, as it converts both the source documents and the user query into dense vector representations that allow semantic matching. The large language model (E) is the third core component, since it consumes the retrieved context along with the query to generate a grounded, natural-language answer. By contrast, a classifier (A) and a rewriter (D) are optional add-ons sometimes used for query routing or query reformulation, but they are not fundamental building blocks of a standard RAG pipeline.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Classifier
Why it's wrong here
A classifier labels or routes content; it neither retrieves documents nor supplies grounding context to the generator. It is tempting because classifiers appear in many ML pipelines, and it would be correct where the task is categorising inputs or filtering results, not the retrieval-generation core of RAG.
- ✓
Vector database
Why this is correct
A vector database stores embedded representations of source documents and performs similarity search, retrieving the passages most relevant to the incoming query. This retrieved context is then supplied to the language model, grounding its response in external knowledge and satisfying RAG's requirement for accurate, up-to-date retrieval beyond the model's training data.
- ✓
Embedding model
Why this is correct
An embedding model converts documents and queries into vectors, enabling semantic similarity search across the indexed corpus. This satisfies RAG's retrieval constraint: relevant passages must be located by meaning rather than keyword matching, so the generator receives grounded context. Without embeddings, retrieval cannot rank passages against the user's question.
- ✗
Rewriter
Why it's wrong here
A rewriter reformulates queries or text; it is an optional enhancement, not a core RAG component alongside retrieval and generation. It is tempting because query rewriting improves recall, and it would be correct when optimising retrieval quality for ambiguous queries rather than defining the system's essential architecture.
- ✓
Large language model
Why this is correct
The large language model is the generation core of a RAG system: it receives the retrieved passages as context and produces the final answer. Without it, retrieval alone cannot synthesise a response, so it satisfies the requirement for a component that performs generation.
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