Generative AI Leader Google Cloud's Generative AI Offerings Practice Question
A media company is evaluating Google Cloud's generative AI offerings for two distinct needs: enabling its journalists to summarize research inside Google Docs, and building a custom internal tool that calls Gemini models programmatically with its own authentication and logging. Which two Google Cloud offerings map to these needs? (Choose two.)
⚠ Common exam trap
The trap here is treating every Google Cloud AI service as interchangeable, when several specialize in transcription, dialogue design, or document extraction rather than content generation.
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 for programmatic Gemini model access
The journalists' in-editor summarization need is met by Gemini for Google Workspace, while the custom programmatic tool requires Vertex AI's APIs, IAM, and logging. The remaining services handle speech, conversational flows, or document parsing and do not provide the requested generative capabilities.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Document AI for parsing research documents
Why it's wrong here
Document AI extracts structured data from forms and documents, which can feed downstream systems, but it does not summarize inside Docs or provide generative model APIs for a custom tool. It addresses document processing rather than the two generative AI needs the media company identified.
- ✗
Cloud Speech-to-Text for transcribing interviews
Why it's wrong here
Cloud Speech-to-Text converts audio into text and is valuable for transcription, but it neither summarizes content in Docs nor provides programmatic generative model access for a custom tool. Choosing it would substitute a narrow speech service for the two broader generative AI capabilities the company actually described.
- ✗
Dialogflow CX for building the internal tool
Why it's wrong here
Dialogflow CX is a conversational agent platform for designing dialogue flows, intents, and telephony or chat experiences. It is not a general-purpose way to call Gemini models from a custom application with the company's own authentication and logging. It would constrain the internal tool to a chatbot paradigm the company did not request.
- ✓
Vertex AI for programmatic Gemini model access
Why this is correct
Vertex AI exposes Gemini models through APIs and SDKs that the company's developers can call from a custom internal tool, complete with IAM authentication, audit logging, and quota management. This satisfies the second need because it supports building bespoke applications on Google Cloud rather than only enhancing existing productivity apps.
- ✓
Gemini for Google Workspace for in-editor assistance
Why this is correct
Gemini for Google Workspace places generative assistance directly inside Docs, so journalists can summarize and refine research without leaving the editor or writing code. It matches the first need precisely because it targets productivity applications rather than custom application development, and administration stays within existing Workspace controls the company already manages.
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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.