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.
A developer wants to build a RAG application using Vertex AI. Which vector database is natively integrated with Vertex AI for storing embeddings?
Firestore
Why wrong: Firestore is a document database, not designed for high-dimensional vector search.
Vertex AI Vector Search
Vector Search is purpose-built for storing and querying embeddings.
Cloud SQL
Why wrong: Cloud SQL is a relational database, not optimized for vector embeddings.
Bigtable
Why wrong: Bigtable is a wide-column NoSQL database, not suited for vector similarity search.
Answer choices
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.
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.
Firestore
Why it's wrong here
Firestore is a document database, not designed for high-dimensional vector search.
Vertex AI Vector Search
Why this is correct
Vector Search is purpose-built for storing and querying embeddings.
Related concept
Read the scenario before looking for a memorised answer.
Cloud SQL
Why it's wrong here
Cloud SQL is a relational database, not optimized for vector embeddings.
Bigtable
Why it's wrong here
Bigtable is a wide-column NoSQL database, not suited for vector similarity search.
Common exam traps
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.
Trap categories for this question
Similar concept trap
Bigtable is a wide-column NoSQL database, not suited for vector similarity search.
Detailed technical explanation
Vertex AI Vector Search uses ScaNN (Scalable Nearest Neighbors) under the hood, an algorithm that efficiently indexes high-dimensional vectors using techniques like product quantization and tree-based partitioning. In a real-world RAG pipeline, embeddings generated by Vertex AI's textembedding-gecko model are stored in Vector Search, and queries retrieve the most semantically similar chunks via approximate nearest neighbor (ANN) search, often achieving sub-10ms latency at scale.
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: 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.
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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