1Z0-1127-25 Using OCI Generative AI Service Practice Question
A company runs batch inference jobs daily using the OCI Generative AI service. The current cost is higher than expected. Which change would most effectively reduce cost while maintaining throughput?
⚠ Common exam trap
Oracle often tests the misconception that reducing token limits or increasing parallelism is the most effective cost-saving measure, when in fact the pricing model change from on-demand to dedicated infrastructure yields the greatest savings for predictable batch workloads.
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
✓
Switch from on-demand to dedicated AI cluster with batch endpoint.
Switching from on-demand to a dedicated AI cluster with a batch endpoint reduces cost because dedicated clusters provide reserved capacity at a lower per-token rate compared to on-demand pay-per-token pricing, and batch endpoints allow you to process multiple inference requests in a single job, amortizing overhead and reducing idle time. This combination directly addresses the high cost of per-request on-demand pricing while maintaining the same throughput for daily batch jobs.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Switch from on-demand to dedicated AI cluster with batch endpoint.
Why this is correct
Dedicated clusters provide lower cost per token for batch workloads and avoid contention.
- ✗
Reduce the max token limit for all requests.
Why it's wrong here
Reducing tokens may affect output quality and does not address cost structure.
- ✗
Use a larger model to reduce retries.
Why it's wrong here
Larger models cost more per token, not less.
- ✗
Increase the number of parallel requests to improve efficiency.
Why it's wrong here
More parallel requests may increase cost due to higher throughput, not reduce it.
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