Generative AI Leader Google Cloud's Generative AI Offerings Practice Question
A logistics company wants to build a generative AI application that answers questions over thousands of internal policy PDFs stored in Cloud Storage. They need Google Cloud to handle document ingestion, chunking, indexing, and retrieval for grounding, while they focus only on the application logic. Which Google Cloud offering should they use?
⚠ Common exam trap
The trap here is assuming any document-processing service provides retrieval, when Document AI and Cloud Vision extract content but do not index or serve passages for grounding.
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 Search
Vertex AI Search is purpose-built for ingesting enterprise documents, chunking and indexing them, and retrieving relevant passages to ground generative responses. It supports Cloud Storage as a data source and abstracts away the retrieval pipeline, so the logistics team can focus on application logic. The other services handle features, document parsing, or image analysis and do not deliver managed semantic retrieval for grounding.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Vertex AI Search
Why this is correct
Vertex AI Search is the managed retrieval and grounding service that ingests documents from sources such as Cloud Storage, handles chunking and indexing, and serves relevant passages to ground generative responses. It removes the need to build a custom retrieval pipeline, letting the logistics team concentrate on application logic. This directly satisfies the ingestion, indexing, and retrieval requirements.
- ✗
Cloud Vision API
Why it's wrong here
Cloud Vision API analyzes images and can perform OCR on scanned pages, but it is not a document retrieval or grounding service. It cannot chunk, index, or semantically retrieve policy text for a generative application. Relying on it would leave the core retrieval problem unsolved and force the team to build indexing and search themselves, which is exactly what they want to avoid.
- ✗
Vertex AI Feature Store
Why it's wrong here
Vertex AI Feature Store manages and serves structured machine learning features for training and online prediction, not unstructured document retrieval. It has no document ingestion, chunking, or semantic indexing for PDFs, so it cannot ground a question-answering application over policy documents. Using it here would require building the entire retrieval stack separately, which contradicts the goal of minimizing effort.
- ✗
Document AI
Why it's wrong here
Document AI extracts structured data from documents using processors such as form parsing and OCR, but it does not provide semantic indexing or retrieval for grounding generative answers. It could help digitize PDFs, yet the company still would need a separate retrieval service. Therefore it does not fulfill the end-to-end ingestion, indexing, and retrieval requirement on its own.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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