1Z0-1127-25 OCI Generative AI Service Practice Question
A data scientist is evaluating the cost of deploying a fine-tuned model for a high-volume production application. They need low latency but are cost-sensitive. Which TWO considerations should they evaluate when choosing between on-demand (shared) and dedicated cluster pricing?
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
✓
The cost per model unit per hour for a dedicated cluster
The cost of model units per hour and the expected token volume help determine whether dedicated or on-demand is more economical. Cluster provisioning time and streaming availability are not direct cost factors.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
The cost per model unit per hour for a dedicated cluster
Why this is correct
Dedicated clusters are billed per model unit hour, which must be compared to on-demand token costs.
- ✗
Whether the model supports streaming responses
Why it's wrong here
Streaming is a feature, not a cost driver.
- ✗
The number of days required to provision the dedicated cluster
Why it's wrong here
Provisioning time affects time-to-market but not the recurring cost comparison.
- ✗
The availability of the model in the OCI Generative AI Playground
Why it's wrong here
Playground availability is irrelevant for production cost analysis.
- ✓
The expected monthly token volume and the on-demand per-token price
Why this is correct
On-demand pricing is per token; comparing with dedicated cluster cost determines break-even point.
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