1Z0-1127-25 Using OCI Generative AI Service Practice Question
A company is using dedicated AI cluster for fine-tuning. Which TWO best practices help optimize cost?
⚠ Common exam trap
Oracle often tests the misconception that larger replica counts or continuous running improve performance, when in fact they only increase cost without accelerating fine-tuning convergence.
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
✓
Manually scale down the cluster when not in use.
Manually scaling down the dedicated AI cluster when not in use directly reduces compute costs by stopping idle GPU/CPU resources. In OCI Generative AI, dedicated AI clusters incur charges for provisioned capacity, so scaling down during inactivity avoids paying for unused infrastructure.
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 largest replica count.
Why it's wrong here
More replicas increase cost unnecessarily.
- ✓
Manually scale down the cluster when not in use.
Why this is correct
Reduces active compute hours.
- ✗
Use the managed serving endpoint instead.
Why it's wrong here
Managed endpoint is not for fine-tuning.
- ✗
Leave the cluster running continuously.
Why it's wrong here
Increases cost due to always-on resources.
- ✓
Use the smallest possible model for the task.
Why this is correct
Smaller models require less compute resources.
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