NCP-AIO Installation and Deployment Practice Question
An administrator is planning to monitor GPU utilization across a large cluster. Which component should be deployed to collect metrics that are compatible with Prometheus?
⚠ Common exam trap
Candidates frequently confuse general Kubernetes metrics servers with GPU-specific telemetry tools, choosing standard kube-state-metrics instead of the dedicated NVIDIA DCGM Exporter.
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
NVIDIA DCGM (Data Center GPU Manager) is the industry-standard tool for collecting GPU health and telemetry data. By deploying the DCGM Exporter, the administrator enables the conversion of hardware telemetry into a Prometheus-friendly format. This integration is vital for observability, allowing teams to set up alerts and dashboards to track GPU usage, power consumption, and memory allocation across the entire fleet of accelerated computing nodes.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
NVIDIA GPU Operator
Why it's wrong here
While the operator can deploy the exporter, the operator itself is an orchestration tool, not the metric collection agent. The exporter is a specific sub-component that must be enabled or installed to provide the necessary telemetry data, so selecting the operator as the primary metric collector is imprecise.
- ✓
NVIDIA DCGM Exporter
Why this is correct
The DCGM Exporter is purpose-built to extract hardware metrics and expose them in a format that Prometheus can scrape. This is the correct choice for integrating GPU telemetry into existing monitoring pipelines, enabling administrators to visualize performance data and effectively manage GPU resources within their Kubernetes infrastructure clusters.
- ✗
NVIDIA Triton Inference Server
Why it's wrong here
The Triton Inference Server provides its own specific metrics regarding model serving latency and throughput, but it does not provide low-level GPU hardware telemetry like power or clock speed. It is an application-level monitoring source, not a comprehensive GPU hardware monitoring solution for the underlying cluster nodes.
- ✗
NVIDIA CUDA Toolkit
Why it's wrong here
The CUDA Toolkit is a development and runtime environment for building accelerated applications. It does not contain telemetry agents or exporters for Prometheus. Installing the toolkit provides the necessary headers and compilers for development but offers no capability to monitor hardware performance metrics in a production cluster environment.
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.