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?
⚠ Common exam trap
The trap here is assuming a CDN or global load balancer solves latency for AI workloads; CDNs cache static assets, not dynamic model inference, so candidates who conflate content delivery with compute placement pick the wrong answer.
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
A multi-region deployment using a platform like Vertex AI lets the company place model endpoints and data processing in each region where users and regulatory requirements exist, minimizing network latency while keeping data resident within sovereign boundaries. Vertex AI supports regional endpoints and data residency controls, so inference and training can occur locally rather than routing everything through one jurisdiction. This directly satisfies both the latency and data sovereignty constraints simultaneously.
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 CDN caches static content at edge locations; it does not host model inference or keep data within regional boundaries, so sovereignty and inference latency remain unaddressed. Regional deployments with data residency controls are required. A CDN suits static asset delivery, not gen AI inference.
- ✓
Multi-region deployment with Vertex AI
Why this is correct
Multi-region deployment with Vertex AI places model endpoints and inference within each region, so requests are served locally rather than crossing borders. This directly satisfies the data sovereignty constraint, since data residency is preserved per region, while regional endpoints cut network round-trip time, addressing the latency requirement.
- ✗
Use a third-party API
Why it's wrong here
Routing inference through a third-party API sends data outside the company's regional boundary, so it cannot satisfy the data sovereignty requirement, and adds network hops that raise latency. Third-party APIs suit rapid prototyping or accessing frontier models when residency and latency constraints are absent.
- ✗
On-premises deployment only
Why it's wrong here
On-premises-only deployment pins all inference to one physical location, so users in other regions incur cross-region latency, and it cannot scale elastically per region. On-premises suits strict residency or air-gapped workloads confined to a single geography, not a multi-region latency-sensitive rollout.
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Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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