1Z0-1127-25 Deploying and Managing Generative AI on OCI Practice Question
Which TWO actions are recommended best practices for managing costs when using OCI Generative AI dedicated AI clusters?
⚠ Common exam trap
It's easy for candidates to think stopping a dedicated AI cluster is a valid cost-saving action, but OCI dedicated AI clusters do not support a 'stop' state—you must terminate the cluster, which loses all configuration and data, making it impractical for intermittent use.
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 preemptible instances for non-critical inference workloads
Preemptible instances in OCI are significantly cheaper than standard instances and are ideal for non-critical inference workloads that can tolerate interruptions. This aligns with cost optimization best practices by allowing you to use spare compute capacity at a reduced rate for tasks that do not require continuous availability.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Provision a fixed number of nodes to handle peak load
Why it's wrong here
Leads to idle resources during low demand.
- ✓
Use preemptible instances for non-critical inference workloads
Why this is correct
Preemptible instances are cheaper and suitable for fault-tolerant tasks.
- ✓
Use autoscaling to adjust nodes based on demand
Why this is correct
Matches capacity to actual usage, reducing cost.
- ✗
Stop the dedicated AI cluster when not in use
Why it's wrong here
Stopping deletes the cluster; you lose data and configurations.
- ✗
Use pay-as-you-go billing instead of preemptible instances
Why it's wrong here
Pay-as-you-go is more expensive; preemptible is cheaper.
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