NCP-AIO Workload Management Practice Question
Which of the following is the most appropriate workload management technique for a bursty AI inference workload that requires low latency but does not need full GPU power for every request?
⚠ Common exam trap
Candidates often mistake general load balancing or horizontal scaling for GPU-level resource management, failing to recognize that MIG is the specific hardware-level solution for partitioning GPUs for multi-tenant inference.
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
✓
Using MIG or fractional GPU sharing to maximize hardware throughput.
NVIDIA Multi-Instance GPU (MIG) combined with time-slicing or fractional GPU sharing is ideal for inference workloads. By partitioning GPUs, the system can handle many smaller, latency-sensitive requests efficiently. This approach balances the need for high-performance hardware with the requirement for multi-tenancy, ensuring that no single inference request monopolizes the entire GPU while maintaining the fast response times required by the application.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Assigning one full physical GPU to every single inference request.
Why it's wrong here
Allocating an entire GPU to a small inference request is a major waste of resources. This limits the total throughput of the cluster and increases the cost per request significantly. It is an inefficient workload management strategy for high-volume, low-latency AI inference services.
- ✓
Using MIG or fractional GPU sharing to maximize hardware throughput.
Why this is correct
MIG and fractional sharing allow multiple inference workloads to run concurrently on a single GPU. This effectively increases the number of available 'virtual' GPUs, maximizing utilization and ensuring that small, bursty requests can be handled with minimal latency, which is essential for cost-effective, high-scale inference services.
- ✗
Configuring the GPU to run in 'Maximum Performance' mode at all times.
Why it's wrong here
Maximum performance mode maximizes power consumption and clock speeds, which is unnecessary for many inference workloads. It does not address the fundamental issue of sharing the GPU across multiple requests. In fact, it can lead to thermal throttling if not managed correctly in a high-density cluster.
- ✗
Implementing a strict FIFO queue for all incoming inference requests.
Why it's wrong here
A FIFO queue can introduce significant latency at the end of the line if a large request arrives. For latency-sensitive inference, a more sophisticated load balancing or scheduling mechanism is required to ensure that small, fast jobs are not blocked by larger, slower ones, regardless of arrival order.
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Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official NVIDIA exam blueprint
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