Generative AI Leader Google Cloud's Generative AI Offerings Practice Question
A logistics company stores delivery exception reports as PDFs in a Cloud Storage bucket. An analyst needs to ask natural-language questions across all the reports and receive answers with citations to the source pages, with minimal development work. Which Google Cloud capability should the analyst use?
⚠ Common exam trap
The trap here is equating document text extraction with question answering; OCR or annotation services produce raw content, but only a grounded search capability delivers cited answers over the corpus.
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 with a data store grounded on the Cloud Storage documents
Vertex AI Search ingests documents directly from Cloud Storage, builds a managed index, and returns natural-language answers grounded in those documents with citations to source pages. The alternatives all require substantial custom development: Pipelines orchestrates custom code, BigQuery ML needs the data loaded and parsed, and Cloud Vision only extracts raw text and features without answering questions.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
BigQuery ML with a remote Gemini model
Why it's wrong here
BigQuery ML can call remote models over structured or unstructured data, but the documents live as PDFs in Cloud Storage, not in BigQuery tables. Loading and parsing them, then wiring retrieval and citation logic, is custom engineering effort, so this approach does not deliver grounded, cited answers with minimal development.
- ✗
Vertex AI Pipelines orchestrating a custom document parser
Why it's wrong here
Vertex AI Pipelines is an orchestration service for machine learning workflows, and using it would require the team to build, schedule, and maintain a custom parsing and retrieval pipeline. That is significant development work and does not provide a ready-made question-answering interface with citations, so it fails the minimal-effort requirement.
- ✗
Cloud Vision API batch annotation of the PDFs
Why it's wrong here
The Cloud Vision API extracts text and labels from images and PDFs but returns raw features rather than answering questions. It provides no retrieval, grounding, or citation mechanism, so the analyst would still need to build the entire question-answering layer, which contradicts the low-effort requirement.
- ✓
Vertex AI Search with a data store grounded on the Cloud Storage documents
Why this is correct
Vertex AI Search can ingest documents from Cloud Storage into a data store, index them, and answer natural-language queries with grounding and citations back to the source content. It is a managed retrieval-augmented generation capability, so the analyst gets cited answers across many PDFs with minimal development work, exactly matching the requirement.
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JA
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
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.