Generative AI Leader Fundamentals of Generative AI Practice Question
A developer wants to build a RAG application using Vertex AI. Which vector database is natively integrated with Vertex AI for storing embeddings?
⚠ Common exam trap
Google Cloud often tests the misconception that any database can store embeddings equally well, but the key differentiator is native vector indexing and ANN search support, which only Vertex AI Vector Search provides among the listed options.
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
✓
Vertex AI Vector Search
Vertex AI Vector Search is the native vector database integrated with Vertex AI for storing and querying embeddings. It is purpose-built for high-dimensional vector similarity search, enabling efficient retrieval in RAG applications without requiring external infrastructure.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Firestore
Why it's wrong here
Firestore is a document database offering no native vector index or embedding similarity search integration with Vertex AI. It is tempting because it is a first-party Google Cloud NoSQL store, and it would be the right choice for storing application metadata, chat sessions or user profiles alongside a RAG pipeline.
- ✓
Vertex AI Vector Search
Why this is correct
Vertex AI Vector Search is Google Cloud's native vector store, purpose-built for the Vertex AI ecosystem, so embeddings generated by Vertex AI models can be indexed and queried without custom integration work. It satisfies the stem's requirement for a natively integrated vector database, unlike third-party options such as Pinecone or open-source alternatives.
- ✗
Cloud SQL
Why it's wrong here
Cloud SQL provides relational storage without native vector indexing or embedding search integrated into Vertex AI. It is tempting because it is a familiar managed database, and it would be correct for storing structured relational data such as document metadata, permissions or audit records supporting a RAG application.
- ✗
Bigtable
Why it's wrong here
Bigtable is a wide-column store for high-throughput time-series and analytical workloads, lacking native embedding storage and similarity search in Vertex AI. It is tempting because it scales to massive datasets, and it would be correct for ingesting high-volume telemetry or serving low-latency key-based lookups.
Go deeper
Related to this question
About these practice questions
One of 1,008 original Generative AI Leader practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.