NCP-GENL Production Monitoring and Reliability Practice Question
A generative AI application built on NVIDIA Triton Inference Server is deployed in a Kubernetes cluster with GPU nodes. The operations team wants to detect silent data corruption in model outputs, which could occur due to GPU memory errors. They plan to implement a monitoring solution using NVIDIA Data Center GPU Manager (DCGM). Which DCGM feature should they enable to detect and alert on GPU memory errors that could lead to silent data corruption?
⚠ Common exam trap
A common mix-up: candidates confuse DCGM diagnostics (run manually) with health checks (continuous monitoring) and assuming that any memory-related test will provide real-time alerting.
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
✓
DCGM health checks with the `memory` watch.
DCGM health checks with the `memory` watch continuously monitor GPU memory for ECC errors and other faults that can cause silent data corruption. This feature can be configured to raise alerts when errors exceed thresholds, enabling proactive remediation. In contrast, diagnostics are run on-demand, utilization metrics reflect workload, and AutoBoost affects clocks. For detecting memory errors that could corrupt LLM outputs, the memory watch is the correct DCGM feature to enable.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
DCGM configuration with `EnableAutoBoost` set to true.
Why it's wrong here
`EnableAutoBoost` is a DCGM configuration setting that controls automatic boosting of GPU clocks for performance. It has no relation to memory error detection or data integrity. Enabling it might improve performance but does not provide monitoring or alerting for memory errors. This option is a distractor because it sounds like a configuration change but does not address the need to detect silent data corruption.
- ✓
DCGM health checks with the `memory` watch.
Why this is correct
DCGM health checks include a `memory` watch that monitors GPU memory for errors such as ECC errors (correctable and uncorrectable). Enabling this watch allows DCGM to detect memory errors that could cause silent data corruption. It can trigger alerts or take corrective actions based on policy. This is the appropriate feature to monitor for GPU memory issues that may affect model output integrity, making it the correct choice for detecting silent data corruption.
- ✗
DCGM profiling metrics with `DCGM_FI_DEV_GPU_UTIL`.
Why it's wrong here
`DCGM_FI_DEV_GPU_UTIL` is a profiling metric that reports GPU utilization percentage. It indicates how busy the GPU is but does not provide any information about memory errors or data corruption. Monitoring utilization is useful for performance tuning but cannot detect silent data corruption. Therefore, this metric is not relevant to the requirement of detecting GPU memory errors that could affect model outputs.
- ✗
DCGM diagnostics with the `-r` option for a full run.
Why it's wrong here
DCGM diagnostics with the `-r` option runs a comprehensive set of tests on the GPU, but it is typically used for validation or troubleshooting, not continuous monitoring. While it can detect memory errors, it is not designed for real-time alerting on silent data corruption during production inference. The health checks with the `memory` watch are better suited for ongoing monitoring and alerting. Diagnostics are usually run on-demand or at startup, not as a continuous production monitoring solution.
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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-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.