Quick Answer
The answer is Vertex AI PaLM API and Vertex AI Vector Search. These two services work together to form the core of a RAG pipeline on Google Cloud: the PaLM API provides the large language model for generation, while Vector Search handles the retrieval step by storing and querying embeddings for semantic similarity search. On the Google Cloud Generative AI Leader exam, this pairing tests your understanding of how retrieval and generation components integrate—a common trap is to confuse Vertex AI Search (a full search service) with Vector Search (the dedicated vector database). Remember that Vector Search is the retrieval engine that fetches relevant context, and the PaLM API is the generator that uses that context to produce grounded answers. A helpful memory tip: think “Vector for retrieval, PaLM for generation”—the two halves of the RAG pipeline.
This Generative AI Leader practice question tests your understanding of business strategies for generative ai solutions. 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 TWO Google Cloud services can be used together to implement a RAG (retrieval-augmented generation) pipeline? (Select 2)
Cloud SQL
Why wrong: Relational database, not suited for vector search.
Vertex AI Vector Search
Provides vector similarity search for retrieval.
Bigtable
Why wrong: NoSQL database, not optimized for similarity search.
Vertex AI PaLM API
Generates responses from retrieved context.
Cloud Functions
Why wrong: Can be used as middleware but not core component.
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 (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.
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.
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.
Related concept
Read the scenario before looking for a memorised answer.
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.
Related concept
Read the scenario before looking for a memorised answer.
Cloud Functions
Why it's wrong here
Can be used as middleware but not core component.
Common exam traps
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.
Trap categories for this question
Similar concept trap
NoSQL database, not optimized for similarity search.
Detailed technical explanation
In a RAG pipeline, the retrieval step typically uses approximate nearest neighbor (ANN) algorithms (e.g., HNSW, IVF) to find the most relevant embeddings. Vertex AI Vector Search supports both brute-force and ANN search with configurable distance metrics (e.g., cosine, dot product), and it integrates directly with Vertex AI PaLM API for generation. A real-world scenario is a customer support chatbot that indexes thousands of product manuals as embeddings; when a user asks a question, the system retrieves the top-k relevant passages and passes them to PaLM API for a grounded, context-aware answer.
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
Business Strategies for Generative AI Solutions — This question tests Business Strategies for Generative AI Solutions — Read the scenario before looking for a memorised answer..
The correct answer is: 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.
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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