1Z0-1127-25 OCI Generative AI Service Practice Question
An organization wants to deploy a chatbot that uses a custom fine-tuned model. They have provisioned a Dedicated AI Cluster with 4 model units. During peak hours, they observe high latency and want to reduce it. What is the most cost-effective change?
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
✓
Increase the number of model units in the dedicated cluster
Adding more model units increases throughput and reduces latency by distributing the load. This is the direct way to improve performance on a dedicated cluster.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Increase the number of model units in the dedicated cluster
Why this is correct
More model units provide additional compute capacity, reducing latency.
- ✗
Reduce the max_tokens parameter in the application
Why it's wrong here
Reducing output tokens may slightly reduce latency but is not a significant change for high throughput.
- ✗
Switch to shared infrastructure to get more capacity
Why it's wrong here
Shared infrastructure may have variable performance and might not provide the needed low latency.
- ✗
Enable response streaming
Why it's wrong here
Streaming improves perceived latency but does not reduce overall processing time.
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