Generative AI Leader Practice Question: Business Strategies for Generative AI Solutions
Which TWO Google Cloud services can be used together to implement a RAG (retrieval-augmented generation) pipeline? (Select 2)
⚠ Common exam trap
Google Cloud often tests the misconception that any database (like Cloud SQL or Bigtable) can serve as a vector store for RAG, but they lack native vector indexing and similarity search, making them unsuitable for efficient retrieval at scale.
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 (option B) is correct because it provides a managed vector database for storing and querying embeddings, which is essential for the retrieval step in a RAG pipeline. It enables semantic similarity search over large datasets, allowing the system to fetch relevant context documents based on a user query.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Cloud SQL
Why it's wrong here
Relational database, not suited for vector search.
- ✓
Vertex AI Vector Search
Why this is correct
Provides vector similarity search for retrieval.
- ✗
Bigtable
Why it's wrong here
NoSQL database, not optimized for similarity search.
- ✓
Vertex AI PaLM API
Why this is correct
Generates responses from retrieved context.
- ✗
Cloud Functions
Why it's wrong here
Can be used as middleware but not core component.
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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.