AI-102 Plan and manage an Azure AI solution Practice Question
You are responsible for managing costs for multiple Azure AI services in your organization. You notice that provisioned throughput units (PTUs) for Azure OpenAI are not fully utilized. What is the most cost-effective action to optimize spending?
⚠ Common exam trap
It's easy for candidates to confuse PTUs with standard Azure auto-scaling concepts (like VM scale sets) and assume auto-scaling is available for PTUs, when in fact PTUs are a fixed-capacity model that requires manual adjustment or a switch to PAYG for cost optimization.
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
✓
Reduce the number of provisioned PTUs or switch to pay-as-you-go
Provisioned throughput units (PTUs) represent a fixed capacity commitment. If PTUs are underutilized, the most cost-effective action is to reduce the number of PTUs or switch to the pay-as-you-go (PAYG) model, which charges only for tokens consumed. This directly aligns with cost optimization by eliminating the fixed cost of unused capacity.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Configure auto-scaling to reduce PTUs during low usage
Why it's wrong here
Auto-scaling cannot reduce provisioned PTUs; PTU deployments are fixed reservations, so scaling rules leave the unused capacity billed. It tempts because auto-scaling genuinely cuts spend for token-based pay-as-you-go deployments, which is the right lever when consumption varies rather than being over-provisioned.
- ✗
Move the resource to a different region with lower pricing
Why it's wrong here
Regional pricing for Azure OpenAI PTUs is uniform, so relocating changes nothing about the unused reservation and adds migration effort. It tempts because region selection does affect cost for many Azure services with differing regional rates, making it the correct move when a service's price genuinely varies by geography.
- ✗
Stop the Azure OpenAI service when not in use
Why it's wrong here
Stopping the Azure OpenAI resource deletes the PTU deployment and its reserved capacity, so unused units are still billed while the service is off. It is tempting because pausing workloads cuts consumption-based costs, but PTUs are provisioned capacity, not pay-per-token usage.
- ✓
Reduce the number of provisioned PTUs or switch to pay-as-you-go
Why this is correct
Underutilised provisioned throughput units incur cost regardless of consumption, so reducing the PTU count or reverting to pay-as-you-go token billing eliminates spend on idle capacity. This directly addresses the stem's unused-PTU constraint, matching cost to actual demand.
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