Quick Answer
The answer is the large language model, vector database, and retrieval system. These three components form the core of a RAG system because the retrieval system first searches a vector database for semantically relevant chunks of external knowledge, which are then fed as context to the large language model to ground its generation in factual data. On the Google Cloud Generative AI Leader exam, this question tests your understanding of how RAG reduces hallucinations by augmenting an LLM with a non-parametric memory store, often appearing as a scenario where you must distinguish between core RAG elements and peripheral tools like embedding models or rerankers. A common trap is confusing the embedding model with the vector database itself—remember, the database stores the embeddings, while the retrieval system performs the search. For a memory tip, think of the “three R’s”: Retrieve (from the vector database), Read (the context), and Respond (with the LLM).
This Generative AI Leader practice question tests your understanding of fundamentals of generative ai. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
Which THREE components are core to a typical Retrieval Augmented Generation (RAG) system?
Classifier
Why wrong: Classifiers are optional, not core to RAG.
Vector database
Stores embeddings and enables similarity search.
Embedding model
Converts documents/queries into vector embeddings.
Rewriter
Why wrong: Query rewriting is optional, not a core component.
Large language model
The LLM generates final responses using retrieved context.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Vector database
A vector database (B) is core to a RAG system because it stores and indexes embeddings of external knowledge chunks, enabling efficient similarity search to retrieve the most relevant context for a user query. This retrieved context is then provided to the LLM to ground its response in factual data, reducing hallucinations and improving accuracy.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Classifier
Why it's wrong here
Classifiers are optional, not core to RAG.
Vector database
Why this is correct
Stores embeddings and enables similarity search.
Related concept
Read the scenario before looking for a memorised answer.
Embedding model
Why this is correct
Converts documents/queries into vector embeddings.
Related concept
Read the scenario before looking for a memorised answer.
Rewriter
Why it's wrong here
Query rewriting is optional, not a core component.
Large language model
Why this is correct
The LLM generates final responses using retrieved context.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
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.
Detailed technical explanation
Under the hood, the embedding model converts both documents and queries into dense vector representations (e.g., using text-embedding-ada-002 or Sentence-BERT), and the vector database performs approximate nearest neighbor (ANN) search using algorithms like HNSW or IVF to quickly find the top-k most similar vectors. In a real-world scenario, a customer support RAG system might use a vector DB like Pinecone or Weaviate to index thousands of product manuals, ensuring the LLM retrieves the exact troubleshooting steps for a specific error code.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
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FAQ
Fundamentals of Generative AI — This question tests Fundamentals of Generative AI — Read the scenario before looking for a memorised answer..
The correct answer is: Vector database — A vector database (B) is core to a RAG system because it stores and indexes embeddings of external knowledge chunks, enabling efficient similarity search to retrieve the most relevant context for a user query. This retrieved context is then provided to the LLM to ground its response in factual data, reducing hallucinations and improving accuracy.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
This Generative AI Leader practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the Generative AI Leader exam.
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