NCP-GENL Production Monitoring and Reliability Practice Question
A generative AI application deployed on NVIDIA Triton Inference Server with NVIDIA AI Enterprise is experiencing silent data corruption in model outputs. The MLOps team needs to implement monitoring to detect such issues early. Which two actions should be taken to enhance observability for silent data corruption? (Choose two.)
⚠ Common exam trap
The trap here is focusing on performance tuning or logging instead of active detection methods like hardware error monitoring and output validation, which are essential for catching silent data corruption.
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
✓
Enable NVIDIA Data Center GPU Manager (DCGM) to monitor GPU ECC error counts and XID errors.
Silent data corruption can stem from hardware faults or software bugs. Monitoring GPU ECC and XID errors via DCGM catches hardware-induced corruption, while output validation against a baseline detects incorrect results regardless of cause. Together, they provide both infrastructure and application-level observability, enabling early detection and mitigation of silent data corruption in production LLM inference.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Enable NVIDIA Data Center GPU Manager (DCGM) to monitor GPU ECC error counts and XID errors.
Why this is correct
DCGM tracks GPU hardware errors such as ECC memory errors and XID errors, which can cause silent data corruption. By monitoring these metrics, the team can detect hardware-level issues that might lead to incorrect model outputs. This is a proactive measure to identify and alert on potential corruption sources before they affect production results.
- ✗
Configure Triton's model warmup to run dummy inferences at startup to stabilize performance.
Why it's wrong here
Model warmup reduces initial latency by preloading kernels but does not detect or prevent silent data corruption. It addresses performance consistency, not data integrity. This action is irrelevant to the scenario of detecting corruption in model outputs during production.
- ✗
Increase the batch size for all inference requests to improve throughput and reduce per-request overhead.
Why it's wrong here
Increasing batch size may improve throughput but does not address silent data corruption. In fact, larger batches could exacerbate memory errors if they exist. This action is unrelated to detecting or preventing corruption and would not enhance observability for output integrity issues.
- ✗
Set up logging of all inference inputs and outputs to a centralized system for later analysis.
Why it's wrong here
While logging inputs and outputs can be useful for post-mortem analysis, it does not actively detect silent data corruption in real time. Without automated validation or hardware error monitoring, corruption may go unnoticed until manual review. This approach is reactive and not a direct monitoring action for early detection.
- ✓
Implement output validation by comparing inference results against a known-good baseline for a sample of requests.
Why this is correct
Output validation involves running periodic checks where outputs are compared to expected results from a trusted baseline. This can catch silent data corruption that manifests as incorrect predictions. By sampling requests and validating outputs, the team can detect anomalies that hardware monitoring might miss, providing a direct check on model output integrity.
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-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.