The answer is to create multiple deployments across different regions and use Azure Traffic Manager to distribute requests. When your Azure OpenAI deployment is already maxed out on provisioned throughput units (PTUs), the bottleneck is the fixed capacity of a single regional endpoint, so scaling vertically is no longer an option. Distributing traffic horizontally across regional deployments via Traffic Manager—using performance or geographic routing—spreads the load, reduces per-deployment contention, and directly lowers latency without requiring a model change. On the AI-102 exam, this scenario tests your understanding of scaling strategies beyond simple PTU increases; a common trap is to suggest switching to a pay-as-you-go model, which doesn’t guarantee lower latency under peak load. Remember the mnemonic “Maxed PTU? Go multi-region with Traffic Manager” to recall that horizontal scaling across regions is the next logical step for latency reduction.
AI-102 Implement generative AI solutions Practice Question
This AI-102 practice question tests your understanding of implement generative ai solutions. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
Your Azure OpenAI application experiences high latency during peak hours. You have already scaled up the deployment to the maximum PTUs. What is the most effective next step to reduce latency?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Create multiple deployments across different regions and use Azure Traffic Manager to distribute requests
When PTU deployment is already maxed out, the bottleneck is the capacity of a single regional deployment. Distributing requests across multiple regional deployments via Azure Traffic Manager (using performance or geographic routing) spreads the load, reducing per-deployment contention and lowering latency. This approach leverages regional redundancy and global load balancing without requiring a model change or sacrificing quality.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✓
Create multiple deployments across different regions and use Azure Traffic Manager to distribute requests
Why this is correct
Geographic load balancing spreads load and reduces latency.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use Azure OpenAI's global deployment with the same PTU
Why it's wrong here
Global deployment doesn't increase capacity beyond PTU limit.
✗
Switch from GPT-4 to GPT-3.5-turbo
Why it's wrong here
This may reduce quality and still not address peak load.
✗
Increase the token limit per request
Why it's wrong here
Increasing token limit can increase latency per request.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates assume 'global deployment' (Option B) provides automatic load distribution, but in reality it still uses a single PTU pool and does not distribute load across regions; the correct approach is to explicitly create multiple regional deployments and route traffic with a traffic manager.
Detailed technical explanation
How to think about this question
Azure Traffic Manager uses DNS-based load balancing to route client requests to the nearest or least-loaded regional endpoint. When combined with multiple PTU deployments, each region handles a fraction of the total traffic, effectively multiplying the available capacity. This is similar to a sharding pattern at the infrastructure layer, where the key is to ensure each deployment has its own PTU allocation and that client retry logic handles failover gracefully.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Implement generative AI solutions — This question tests Implement generative AI solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Create multiple deployments across different regions and use Azure Traffic Manager to distribute requests — When PTU deployment is already maxed out, the bottleneck is the capacity of a single regional deployment. Distributing requests across multiple regional deployments via Azure Traffic Manager (using performance or geographic routing) spreads the load, reducing per-deployment contention and lowering latency. This approach leverages regional redundancy and global load balancing without requiring a model change or sacrificing quality.
What should I do if I get this AI-102 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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