1Z0-1127-25 OCI Generative AI Service Practice Question
An organization wants to use OCI Generative AI for a high-volume summarization workload. They estimate 10 million tokens per month and need consistent low latency. Which pricing model is most cost-effective?
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
✓
Provision a Dedicated AI Cluster with model units
On-demand pricing can be expensive at high volumes. Dedicated clusters offer predictable cost per model unit and low-latency dedicated inference, making them more cost-effective for high-volume, latency-sensitive workloads. Pay-as-you-go (on-demand) is suitable for low or variable usage.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use the free tier
Why it's wrong here
Free tier has limited tokens and cannot sustain 10M tokens.
- ✗
Use OCI Data Science notebook sessions
Why it's wrong here
Notebooks are for development, not production summarization workloads.
- ✗
On-demand token-based pricing
Why it's wrong here
At 10M tokens per month, on-demand costs may exceed dedicated cluster pricing, and latency is not guaranteed.
- ✓
Provision a Dedicated AI Cluster with model units
Why this is correct
Dedicated clusters provide consistent low latency and predictable cost, better for high-volume workloads.
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