1Z0-1127-25 OCI Generative AI Service Practice Question
A company wants to reduce costs for a high-volume, latency-tolerant text generation workload using OCI Generative AI. Which TWO strategies should they consider?
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
✓
Use on-demand (pay-per-token) pricing instead of a dedicated AI cluster
On-demand pricing (pay-per-token) and using a smaller/faster model (like Command R instead of R+) are cost-saving measures. Dedicated clusters increase cost; the largest model is more expensive; increasing temperature doesn't affect pricing.
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 on-demand (pay-per-token) pricing instead of a dedicated AI cluster
Why this is correct
On-demand pricing is cost-effective for variable or high-volume workloads without dedicated infrastructure commitment.
- ✗
Provision a dedicated AI cluster for consistent throughput
Why it's wrong here
Dedicated clusters incur fixed costs regardless of usage, which can be higher for latency-tolerant workloads.
- ✗
Increase the temperature parameter to reduce token usage
Why it's wrong here
Temperature does not affect token count or cost; it only influences randomness.
- ✗
Use the largest available model to ensure best quality
Why it's wrong here
Larger models are more expensive per token; for latency-tolerant, cost-sensitive workloads, smaller models may suffice.
- ✓
Use a smaller, faster model like Cohere Command R instead of Command R+
Why this is correct
Smaller models consume fewer resources per token, reducing per-token cost for the same workload.
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