NCP-GENL Production Monitoring and Reliability Practice Question
You are deploying a large language model on NVIDIA Triton Inference Server in a Kubernetes cluster. To ensure high availability and reliability, which TWO practices should you implement? (Choose two.)
⚠ Common exam trap
The trap here is focusing on performance optimizations like model warmup or multiple instances, which do not provide redundancy across failures.
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
✓
Set up health checks and readiness probes for the Triton server pods.
High availability in Kubernetes requires redundancy and health monitoring. Deploying multiple replicas across nodes ensures that a single node failure does not take down the service. Readiness probes ensure traffic is only sent to healthy pods. Together, these practices minimize downtime and maintain reliable inference serving.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Enable Triton's model warmup to reduce cold-start latency.
Why it's wrong here
Model warmup reduces latency for the first requests after a model loads, but it does not provide high availability. It is a performance optimization, not a redundancy mechanism. If the node fails, warmup does not help maintain service uptime.
- ✗
Use a single large GPU instance to maximize performance and reduce complexity.
Why it's wrong here
Using a single large GPU creates a single point of failure. If that GPU or node fails, the entire service goes down. High availability requires redundancy, so relying on one instance is contrary to the goal.
- ✓
Set up health checks and readiness probes for the Triton server pods.
Why this is correct
Health checks and readiness probes allow Kubernetes to detect unhealthy pods and stop routing traffic to them, and to restart failed pods. This ensures that only healthy replicas serve requests, improving reliability and availability. It is a standard practice for production deployments.
- ✗
Configure multiple model instances per GPU to increase concurrency.
Why it's wrong here
Multiple instances can improve throughput by allowing concurrent execution, but they do not directly provide high availability. If the GPU or node fails, all instances on that GPU become unavailable. High availability requires redundancy across nodes, not just concurrency within a node.
- ✓
Deploy the Triton server as a Kubernetes Deployment with multiple replicas across nodes.
Why this is correct
Running multiple replicas across nodes ensures that if one node fails, other replicas continue to serve requests. Kubernetes can reschedule failed pods, and a Service can load balance across healthy replicas. This is a fundamental practice for high availability in containerized environments.
About these practice questions
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official NVIDIA exam blueprint
This NCP-GENL practice question is part of Courseiva's free NVIDIA certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the NCP-GENL exam.