1Z0-1127-25 Deploying and Managing Generative AI on OCI Practice Question
A team deploys a generative AI model endpoint and notices intermittent 429 Too Many Requests errors. The endpoint is configured with auto-scaling using a dedicated AI cluster. What is the most likely cause?
⚠ Common exam trap
Watch out — candidates often confuse client-side rate limiting (API Gateway) with server-side capacity issues (auto-scaling lag), but the dedicated AI cluster configuration points directly to insufficient scaling policy aggressiveness.
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
✓
The auto-scaling policy is not aggressive enough
The 429 Too Many Requests error indicates that the endpoint is receiving more requests than it can handle. With auto-scaling enabled on a dedicated AI cluster, the most likely cause is that the auto-scaling policy is not aggressive enough to keep up with the request rate, meaning it scales up too slowly or has insufficient maximum instance limits to handle the traffic spike.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The model's context window exceeded
Why it's wrong here
That would result in a 400 Bad Request or similar error.
- ✗
Insufficient storage on the cluster
Why it's wrong here
Storage issues do not cause 429 errors.
- ✓
The auto-scaling policy is not aggressive enough
Why this is correct
Auto-scaling may not be scaling up quickly enough to handle traffic spikes, leading to throttling.
- ✗
Rate limiting at the OCI API Gateway
Why it's wrong here
While possible, the question specifies the endpoint is configured with auto-scaling, making D more likely.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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