Generative AI Leader Practice Question: Business Strategies for Generative AI Solutions
A global company deploying gen AI across multiple regions needs to minimize latency and comply with data sovereignty. What architecture should they adopt?
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
✓
Multi-region deployment with Vertex AI
Multi-region deployment with Vertex AI (option B) is correct because it allows serving models in close proximity to users across different regions, reducing latency while complying with data sovereignty requirements. Option A (single global deployment with CDN) is incorrect because a single deployment may violate data residency laws and CDN does not address compute locality for model inference. Option C (third-party API) is incorrect because relying on external APIs may not provide the necessary control over data placement and may introduce additional compliance risks. Option D (on-premises only) is incorrect because it is costly, limits scalability, and may not be feasible for global reach.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Single global deployment with CDN
Why it's wrong here
A single global deployment with CDN reduces latency for static content but does not address data sovereignty since data is stored in one location, potentially violating local regulations.
- ✓
Multi-region deployment with Vertex AI
Why this is correct
Multi-region deployment with Vertex AI allows serving models from multiple regions, reducing latency for users worldwide and ensuring data stays within jurisdiction boundaries, complying with data sovereignty.
- ✗
Use a third-party API
Why it's wrong here
Using a third-party API may offload some infrastructure concerns but often lacks granular control over data residency and can introduce vendor lock-in and latency issues if not deployed regionally.
- ✗
On-premises deployment only
Why it's wrong here
On-premises deployment only gives full control over data but is expensive, hard to scale globally, and does not leverage managed AI services like Vertex AI, increasing operational complexity.
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