NCP-AIO Workload Management Practice Question
Which component in the NVIDIA AI ecosystem is responsible for monitoring and reporting GPU telemetry data, such as power usage, temperature, and utilization, to Prometheus?
⚠ Common exam trap
Candidates often confuse the exporter with the DCGM agent itself. The agent collects data, but the exporter is the specific component that translates it for Prometheus scraping.
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
✓
The NVIDIA DCGM Exporter.
The NVIDIA DCGM Exporter is the standard component designed to collect GPU metrics via the Data Center GPU Manager (DCGM) and export them into a format that Prometheus can scrape. This is vital for workload management because it provides the real-time observability required to trigger auto-scaling or identify jobs that are failing to utilize allocated GPU resources effectively across the cluster.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The NVIDIA Device Plugin.
Why it's wrong here
The device plugin is primarily for resource discovery and registration with the Kubernetes scheduler. It does not handle telemetry collection or metrics exporting. Using the device plugin for monitoring would be inefficient as it lacks the specialized hooks into DCGM required for granular GPU performance reporting.
- ✓
The NVIDIA DCGM Exporter.
Why this is correct
The DCGM Exporter is purpose-built to interface with the underlying NVIDIA driver and DCGM to gather detailed telemetry. It exposes these metrics as Prometheus-formatted data, which is essential for monitoring the health and performance of GPU workloads within a cluster and making data-driven infrastructure management decisions.
- ✗
The Kubernetes Kubelet.
Why it's wrong here
The Kubelet manages the lifecycle of pods and containers but does not have native, deep visibility into NVIDIA GPU hardware metrics. It relies on external exporters, such as the DCGM Exporter, to provide these specialized hardware performance insights that are critical for managing complex AI/ML workloads.
- ✗
The NVIDIA Container Runtime.
Why it's wrong here
The container runtime's role is to ensure that the container can interface with the GPU, not to monitor or report telemetry. Monitoring must be a separate, dedicated process to avoid introducing overhead into the container execution path and to maintain a clean separation of duties within the stack.
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.