AI-200 Containerized AI Workloads Practice Question
You are optimizing a large language model container inference deployment on AKS using GPU-enabled nodes (NC-series). The inference server experiences frequent out-of-memory errors on the GPU device itself during high context lengths. Which Kubernetes configuration metric should you monitor and alert on?
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
✓
container_gpu_memory_used_bytes via NVIDIA DCGM Exporter
Monitoring NVIDIA GPU memory usage via DCGM (Data Center GPU Manager) metrics in Prometheus/Grafana is critical for detecting GPU out-of-memory errors.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
container_memory_working_set_bytes via kubelet
Why it's wrong here
kubelet metrics track host CPU/RAM usage, not dedicated VRAM inside the GPU accelerator.
- ✗
container_cpu_cfs_throttled_periods_total
Why it's wrong here
This metric monitors CPU throttling, which does not reflect GPU VRAM exhaustion.
- ✗
node_net_bytes_total
Why it's wrong here
This tracks network interface throughput, unrelated to GPU memory.
- ✓
container_gpu_memory_used_bytes via NVIDIA DCGM Exporter
Why this is correct
NVIDIA DCGM exporter tracks GPU-specific memory consumption metrics directly.
About these practice questions
This AI-200 question is part of Courseiva's 507-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed August 2026 · checked against the official Microsoft exam blueprint
This AI-200 practice question is part of Courseiva's free Microsoft 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 AI-200 exam.