Exam NCA-AIIO Topic 1 Question 26 Discussion

Actual exam question for NVIDIA's NCA-AIIO exam
Question #: 26
Topic #: 1
You are managing an AI training workload that requires high availability and minimal latency. The data is stored across multiple geographically dispersed data centers, and the compute resources are provided by a mix of on-premises GPUs and cloud-based instances. The model training has been experiencing inconsistent performance, with significant fluctuations in processing time and unexpected downtime. Which of the following strategies is most effective in improving the consistency and reliability of the AI training process?

Suggested Answer: B Vote an answer

Implementing a hybrid load balancer (B) dynamically distributes workloads across cloud and on-premises GPUs, improving consistency and reliability. In a geographically dispersed setup, latency and downtime arise from uneven resource utilization and network variability. A hybrid load balancer (e.g., using Kubernetes with NVIDIA GPU Operator or cloud-native solutions) optimizes workload placement based on availability, latency, and GPU capacity, reducing fluctuations and ensuring high availability by rerouting tasks during failures.
* Upgrading GPU drivers(A) improves performance but doesn't address distributed system issues.
* Single-cloud provider(C) simplifies management but sacrifices on-premises resources and may not reduce latency.
* Centralized data(D) reduces network hops but introduces a single point of failure and latency for distant nodes.
NVIDIA supports hybrid cloud strategies for AI training, making (B) the best fit.

by Armand at Feb 24, 2026, 05:43 PM

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