Generative AI Leader Google Cloud's Generative AI Offerings Practice Question
A media company wants to let its editors query a large archive of internal video transcripts using everyday conversational questions, and the app must return grounded answers that cite the exact source clips. The team has no machine learning engineers and wants the least operational overhead. Which Google Cloud offering should they use?
⚠ Common exam trap
The trap here is assuming that calling a powerful Gemini model directly automatically grounds answers in a private transcript archive, when grounding requires an indexed data store.
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 connected transcript data store
A managed search-and-grounding service is the right fit when business users need conversational answers over an indexed corpus with citations and no ML engineering effort. Indexing transcripts into a data store and letting the search service handle retrieval, grounding and citation generation satisfies both the accuracy and low-overhead constraints. Custom pipelines or raw model calls push that work back onto the team.
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 reference
Why it's wrong here
BigQuery ML remote models let SQL users invoke Gemini over tabular data, but they are not designed to serve a conversational, citation-producing search interface over unstructured transcripts. Editors would need to write SQL and build a UI around it, and grounded citation of specific clips is not a native behavior of that path, so it does not fit the requirement.
- ✗
Vertex AI Pipelines with a custom retrieval component
Why it's wrong here
Vertex AI Pipelines orchestrates ML workflows such as training and batch processing; it does not itself index content or answer natural-language questions. Building retrieval on top of Pipelines would require the team to author and maintain custom components and serving, which contradicts the stated goal of minimal operational overhead for a conversational, citation-backed search experience.
- ✓
Vertex AI Search with a connected transcript data store
Why this is correct
Vertex AI Search provides out-of-the-box grounded retrieval over indexed enterprise content, and its data stores support unstructured sources such as transcripts, returning answers with citations to source documents. Because it is a managed offering, no model training or serving infrastructure is required, matching the low-overhead and grounded-citation requirements of the editorial archive scenario.
- ✗
Gemini via the Gemini API in a stateless prompt loop
Why it's wrong here
Calling Gemini directly generates text but does not index the transcript archive or guarantee that answers are grounded in it; the model cannot cite clips it has never retrieved. A prompt loop would also require the editors' tooling to implement retrieval, chunking and citation logic themselves, adding exactly the engineering burden the scenario says the team wants to avoid.
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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
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.