NCP-GENL Model Deployment Practice Question
Exhibit
log_output: [W] [TRT-LLM] [Performance] Request 1042 processing time exceeded 500ms. [W] [TRT-LLM] [Performance] KV Cache eviction detected for sequence 1042. [E] [TRT-LLM] [Memory] Out of Memory: Failed to allocate 128MB.
Refer to the exhibit. What is the most likely cause of the failure based on the log entries?
⚠ Common exam trap
Candidates often misidentify the error as a general memory leak or a model weight loading issue, failing to correlate the specific symptom of KV cache eviction with long-context generation demands.
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
✓
The KV cache size is exceeding the allocated GPU memory limit.
The logs indicate high processing latency followed by KV cache eviction and finally an OOM error. This sequence suggests that the system is running out of VRAM due to the growing KV cache during long-context generation. As the context length increases, the memory required for the KV cache exceeds the available capacity, forcing evictions, slowing down processing, and ultimately triggering an Out of Memory crash.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The model weights are corrupted in the repository.
Why it's wrong here
Corrupted weights would trigger a model loading failure or a runtime error during the initial inference attempt. The log shows that the system was processing requests successfully until the memory reached capacity during generation, pointing toward a dynamic memory management issue rather than a static file corruption problem.
- ✓
The KV cache size is exceeding the allocated GPU memory limit.
Why this is correct
LLMs require a significant amount of memory for the KV cache to store key-value pairs of previous tokens. When the sequence length or batch size grows too large for the allocated memory, the system exhausts VRAM, leading to performance degradation, evictions, and eventual OOM termination during inference.
- ✗
Network latency is causing the client-side timeout.
Why it's wrong here
The logs show explicit internal errors related to KV cache management and memory allocation, not network time-outs. While network latency can slow down request delivery, it does not cause local GPU memory allocation failures, which are strictly handled by the inference engine running on the hardware.
- ✗
The GPU driver version is incompatible with TensorRT.
Why it's wrong here
Driver incompatibility typically prevents the initialization of the CUDA runtime, causing the service to fail to start or report driver-level errors. The log shows that the model reached a stage where it was actively processing sequences, indicating that the driver and environment were initially configured correctly.
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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.