Generative AI Leader Google Cloud's Generative AI Offerings Practice Question
A media company is using Vertex AI to build a generative AI application that creates personalized news summaries. They want to ensure the model's outputs are factually grounded in their curated article database and that the application can scale to thousands of concurrent users. Which two Google Cloud services should they use to achieve these goals? (Choose two.)
⚠ Common exam trap
A common mix-up: candidates confuse data processing or orchestration services like Cloud Dataflow or Vertex AI Pipelines with the retrieval and serving components required for a grounded, scalable generative AI application.
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 grounds the generative summaries in the curated article database, ensuring factual accuracy. Vertex AI Endpoints provides the scalable online serving infrastructure needed to handle thousands of concurrent users. Together, they address both grounding and scalability, while the other services focus on data processing, content delivery, or pipeline orchestration.
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 provides managed retrieval over the curated article database, enabling the generative model to ground its summaries in factual content. It handles indexing, semantic search, and integration with Gemini, which directly supports the requirement for factual grounding and reduces hallucinations.
- ✗
Cloud Dataflow
Why it's wrong here
Cloud Dataflow is a data processing service for batch and stream pipelines, not for serving generative AI models or grounding outputs. While it could preprocess articles, it does not provide retrieval or scalable online inference, so it does not directly address the grounding and scaling requirements.
- ✓
Vertex AI Endpoints
Why this is correct
Vertex AI Endpoints allows deploying Gemini models for online prediction with autoscaling, which is necessary to handle thousands of concurrent users. It provides a scalable, low-latency serving layer that integrates with other Vertex AI components, ensuring the application can meet demand.
- ✗
Cloud CDN
Why it's wrong here
Cloud CDN caches web content at the edge to reduce latency for static assets, but it does not ground generative model outputs or serve AI inference. It is irrelevant to ensuring factual summaries or scaling model predictions for concurrent users.
- ✗
Vertex AI Pipelines
Why it's wrong here
Vertex AI Pipelines orchestrates ML workflows such as training and evaluation, but it is not used for real-time grounding of generative outputs or for serving models at scale. It would not help the media company achieve factual grounding or handle concurrent inference requests.
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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.