NCP-AIO Workload Management Practice Question
Which approach is most effective for scaling an inference workload that experiences sudden, unpredictable spikes in request volume?
⚠ Common exam trap
Candidates frequently choose CPU-based metrics for autoscaling, not realizing that GPU-bound workloads often saturate the GPU while CPU usage remains low, rendering standard HPA configurations ineffective for scaling.
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
✓
Implementing HPA with custom metrics provided by DCGM Exporter.
Predictive or reactive autoscaling based on custom GPU metrics (like utilization) allows the cluster to adjust replica counts in real-time. By monitoring the GPU load and scaling the inference pods accordingly, the system maintains low latency during spikes while keeping costs low during idle periods. This responsiveness is vital for production AI services where performance SLAs are tied directly to user experience.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Setting a fixed number of replicas to match peak expected load.
Why it's wrong here
Fixed scaling is inefficient and costly. Provisioning for peak load means the cluster will be significantly over-provisioned during off-peak hours, leading to wasted hardware resources and higher costs. It fails to address the flexibility required for unpredictable traffic patterns, which are typical in most production AI inference services today.
- ✗
Using a load balancer to redirect traffic to an idle cluster.
Why it's wrong here
Redirecting traffic to a separate cluster is a complex disaster recovery or multi-region strategy. It is not an effective way to handle local scaling spikes, as it introduces latency and requires complex state synchronization, making it a poor choice for simple autoscaling needs within a single infrastructure environment.
- ✓
Implementing HPA with custom metrics provided by DCGM Exporter.
Why this is correct
HPA using custom GPU metrics allows for precise scaling triggered by actual hardware usage. As request volume spikes, GPU utilization increases, and the HPA automatically triggers the deployment of additional inference pods. This ensures that the system scales only when needed, maintaining optimal performance while minimizing resource waste during quiet periods.
- ✗
Increasing the memory allocation for each inference container.
Why it's wrong here
Memory allocation is generally determined by the model size, not the request volume. Simply increasing memory per container does nothing to improve request throughput during spikes. It will result in unused memory overhead without providing the additional compute power required to process the increased number of concurrent incoming requests.
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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
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