NCP-GENL Production Monitoring and Reliability Practice Question
An enterprise is running a mission-critical generative AI application on an NVIDIA DGX cluster. The MLOps team notices occasional silent GPU memory corruption during long-running inference jobs that do not trigger hard crashes. Which monitoring tool and strategy should be utilized for early detection?
⚠ Common exam trap
Engineers often rely solely on standard Kubernetes pod health checks, completely missing hardware-level silent errors that occur beneath the container runtime layer.
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
✓
Deploy NVIDIA Data Center GPU Manager (DCGM) with Prometheus exporter to monitor XID errors and hardware ECC events continuously.
DCGM (Data Center GPU Manager) provides specialized diagnostic tests and continuous health monitoring, including ECC error tracking and XID error detection. Configuring DCGM to raise alerts on uncorrectable memory errors or specific XID failure codes allows operators to isolate failing GPUs before they impact production workloads.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Deploy NVIDIA Data Center GPU Manager (DCGM) with Prometheus exporter to monitor XID errors and hardware ECC events continuously.
Why this is correct
DCGM provides specialized diagnostic tests and continuous health monitoring, including ECC error tracking and XID error detection. Configuring DCGM to raise alerts on uncorrectable memory errors or specific XID failure codes allows operators to isolate failing GPUs before they impact production workloads.
- ✗
Increase the frequency of client-side HTTP ping probes sent from the load balancer to the API gateway.
Why it's wrong here
HTTP ping probes only check if the web server process is accepting network connections and cannot detect underlying GPU memory corruption or hardware degradation. The server will continue responding while returning corrupted or invalid inference tokens.
- ✗
Write a custom bash script that runs nvidia-smi every five minutes and parses plain text output for warning strings.
Why it's wrong here
Parsing nvidia-smi text cannot surface ECC errors or row remapping, and five-minute polling misses corruption occurring between samples. It tempts because nvidia-smi is readily available, but continuous DCGM-based telemetry with XID and ECC counters is required for early detection.
- ✗
Rely on standard Kubernetes node liveness probes to automatically restart pods when system memory usage exceeds 90%.
Why it's wrong here
Liveness probes only detect process or node unresponsiveness, so silent memory corruption passes undetected until outputs are wrong. It tempts as an existing Kubernetes health mechanism, yet early detection requires GPU-level telemetry such as DCGM with ECC error and row-remap monitoring.
About these practice questions
This NCP-GENL question is part of Courseiva's 352-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 September 2026 · checked against the official NVIDIA exam blueprint
This NCP-GENL 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-GENL exam.