Let’s go over an example question that could appear on Google Cloud’s new Generative AI Leader exam, which was released in May 2025.
If you want more preparation help like this, you can check out my Generative AI Leader practice exams.
The Question
A large hospital network wants to improve how medical staff access information scattered across electronic health records, equipment maintenance files, and policy documents. They are looking for a way to let clinicians quickly retrieve data without navigating through multiple disconnected systems. Which benefit of Google Cloud Agentspace best fits this scenario?
A. Agentspace allows employees to query and use enterprise data by creating AI agents that retrieve information from different sources
B. Agentspace is primarily designed for training custom deep learning models on specialized compute hardware like TPUs and GPUs
C. Agentspace focuses on managing real-time predictive analytics workflows for structured clinical datasets at scale
D. Agentspace is used for building patient-facing chatbots that provide automated responses in customer service contexts
Explanation
One of the biggest challenges in large organizations is how fragmented internal information becomes over time. Hospitals in particular deal with medical records, compliance documentation, and even equipment logs that live in different systems. This makes it difficult for clinicians to get a full picture quickly, especially when they are pressed for time. Google Cloud introduced Agentspace to help solve this type of problem. It is part of Google Cloud’s broader generative AI ecosystem and is designed to let enterprises create AI-powered agents that connect with internal knowledge sources.
In this case, the hospital network needs a way to unify access to multiple systems so that clinicians do not have to perform manual searches across each one. The correct answer is:
A. Agentspace allows employees to query and use enterprise data by creating AI agents that retrieve information from different sources
This option directly addresses the business need of connecting fragmented data systems and returning useful results through AI agents. The other answers may sound reasonable, but they describe capabilities outside of what Agentspace provides. Training deep learning models on GPUs or TPUs is about infrastructure, which is handled by other parts of Google Cloud. Predictive analytics pipelines align more with Vertex AI, which is focused on building and operationalizing ML models. Patient-facing chatbots are part of customer service solutions, not internal staff tools.
Each Google Cloud service has a specific role, and knowing which one maps to a particular business requirement is the key to answering exam questions like this. In scenarios about unifying enterprise knowledge across different systems, Agentspace is the right fit.
More practice
I’ve got more Generative AI Leader practice questions that you can review to make sure you pass the real exam.