NCP-AIO Workload Management Practice Question
A DevOps engineer needs to monitor GPU health metrics in real-time for workload management. Which tool provides the most granular visibility into GPU utilization and power consumption for individual containers?
⚠ Common exam trap
Candidates frequently choose generic Kubernetes metrics or standard Prometheus exporters. These tools often lack the specific hooks needed to read deep GPU hardware registers like power draw and memory bandwidth.
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
✓
NVIDIA DCGM Exporter.
The NVIDIA DCGM (Data Center GPU Manager) Exporter is the industry-standard tool for collecting fine-grained GPU telemetry. By integrating with Prometheus and Grafana, it allows administrators to track metrics like power usage, temperature, and utilization at the container level. This granular data is vital for proactive workload management, allowing for autoscaling based on actual hardware demand rather than generic CPU metrics.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Kubernetes Horizontal Pod Autoscaler (HPA) with CPU metrics.
Why it's wrong here
The standard HPA relies on CPU and memory metrics, which are often poor proxies for GPU-accelerated workload demands. Using CPU-based metrics for GPU-intensive workloads often results in inaccurate scaling, leading to either resource waste or performance degradation when the GPU becomes the primary bottleneck in the system.
- ✓
NVIDIA DCGM Exporter.
Why this is correct
The DCGM Exporter provides comprehensive, hardware-specific metrics that are essential for deep visibility into GPU performance. It enables the capture of precise utilization data per container, which allows administrators to make data-driven decisions regarding resource allocation, capacity planning, and the optimization of AI training and inference workloads.
- ✗
Standard Linux 'top' command.
Why it's wrong here
The 'top' command provides system-level process information but lacks the ability to report on NVIDIA-specific GPU metrics such as SM utilization, memory bandwidth, or power draw. It is ineffective for monitoring hardware-accelerated workloads, providing no insight into whether the GPU itself is performing efficiently or idling.
- ✗
Docker stats command.
Why it's wrong here
While 'docker stats' is useful for container-level resource monitoring, it is limited to CPU, memory, and basic I/O usage. It does not provide the advanced telemetry required to monitor GPU health or performance, making it inadequate for managing the specific requirements of AI operations and hardware-accelerated tasks.
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-AIO 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-AIO exam.