1Z0-1127-25 Deploying and Managing Generative AI on OCI Practice Question
A company is deploying a large generative AI model on OCI using GPU compute instances. They want to optimize inference cost while maintaining acceptable latency. Which TWO strategies should they implement?
⚠ Common exam trap
Oracle often tests the misconception that 'bigger GPU instances always improve performance' or that 'provisioned concurrency applies to all OCI services,' when in fact it is specific to serverless compute and irrelevant to GPU inference endpoints.
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
✓
Select the smallest GPU instance type that meets latency requirements.
Selecting the smallest GPU instance type that meets latency requirements directly reduces compute cost per inference without sacrificing user experience. This aligns with OCI's pay-as-you-go GPU pricing, where larger instances incur higher hourly costs. The key is to right-size the GPU based on model memory footprint and inference throughput, not to over-provision.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Enable provisioned concurrency on all models.
Why it's wrong here
Provisioned concurrency keeps instances always on, increasing cost.
- ✓
Select the smallest GPU instance type that meets latency requirements.
Why this is correct
Choosing appropriate instance size avoids paying for unused capacity.
- ✗
Increase the max-tokens parameter to generate longer responses.
Why it's wrong here
Longer responses increase compute time and cost.
- ✗
Deploy the model on multiple large GPU instances to handle peak load.
Why it's wrong here
Over-provisioning increases cost without optimizing.
- ✓
Use an inference endpoint with auto-scaling to match demand.
Why this is correct
Auto-scaling adjusts resources to actual load, reducing cost during low demand.
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