NCP-AIO Workload Management Practice Question
Which component of the NVIDIA GPU Operator is responsible for monitoring GPU health and reporting telemetry data to the Kubernetes control plane?
⚠ Common exam trap
Candidates often confuse management components like the GPU Operator or device plugin with the specialized telemetry collection agent, mixing up scheduling logic with metrics gathering.
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 DCGM Exporter is the primary tool within the NVIDIA GPU Operator ecosystem for collecting metrics. It interacts with the Data Center GPU Manager (DCGM) to gather telemetry such as utilization, temperature, and memory health. This is vital for AI Operations because it provides the observability necessary to trigger auto-scaling, identify failing hardware, and ensure that training jobs are performing optimally within the cluster environment.
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 Device Plugin.
Why it's wrong here
The device plugin is responsible for advertising GPU resources to the Kubernetes scheduler and facilitating container access. While essential for lifecycle management, it does not provide telemetry or performance monitoring. It handles the 'what' and 'where' of resource scheduling, but not the 'how well' of operational health metrics.
- ✓
NVIDIA DCGM Exporter.
Why this is correct
The DCGM Exporter collects detailed GPU metrics and exposes them in a Prometheus-compatible format. This allows administrators to monitor GPU health, performance, and power consumption. It is the core monitoring component that bridges the gap between hardware telemetry and the observability stack in a containerized AI infrastructure.
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NVIDIA Container Toolkit.
Why it's wrong here
The Container Toolkit enables the injection of GPU resources into containers at runtime. It provides the necessary drivers and libraries for the container runtime to interact with the hardware. It is a fundamental building block for execution, not a monitoring or telemetry service for tracking hardware health metrics.
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NVIDIA Node Feature Discovery.
Why it's wrong here
Node Feature Discovery (NFD) is responsible for detecting hardware characteristics and labeling nodes accordingly. While it helps the scheduler make informed decisions based on hardware capabilities, it does not monitor the health, performance, or real-time utilization metrics of the GPU devices after the pods have been successfully scheduled.
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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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