NCA-GENL Data Analysis and Visualization Practice Question
Exhibit
LOG: [INFO] Epoch 10: Step 5000 | LR: 0.0001 | Loss: 1.24 | GPU_MEM: 38.5GB/40GB | UTIL: 98.2% LOG: [INFO] Epoch 10: Step 5010 | LR: 0.0001 | Loss: 1.25 | GPU_MEM: 39.9GB/40GB | UTIL: 99.1% LOG: [WARNING] Epoch 10: Step 5020 | LR: 0.0001 | Loss: 1.24 | GPU_MEM: 40.0GB/40GB | UTIL: 99.5%
Refer to the exhibit. What is the primary risk indicated by the provided logs for this training job?
⚠ Common exam trap
Candidates often misinterpret the logs as a training convergence issue or a software bug. They fail to recognize the specific pattern of linear memory growth leading to a hard limit.
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
✓
An Out of Memory (OOM) error is imminent.
The logs indicate that GPU memory usage is steadily climbing toward the physical limit of 40GB, reaching 100% capacity at step 5020. This indicates a potential memory leak or an unoptimized batch size, which will soon result in an 'Out of Memory' (OOM) error. Detecting this upward trend early allows developers to adjust the batch size or employ techniques like gradient accumulation before the training job crashes, preventing loss of progress and expensive compute time.
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 is converging too quickly to be stable.
Why it's wrong here
The loss value remains stable around 1.24, indicating consistent learning progress rather than rapid, unstable convergence. The concern shown in the logs is related to hardware resource utilization, specifically memory allocation, rather than the mathematical stability of the optimization process or the rate at which the loss is decreasing.
- ✓
An Out of Memory (OOM) error is imminent.
Why this is correct
The GPU memory consumption is monotonically increasing with each logged step, reaching the device capacity of 40GB. This trend confirms that the current workload is unsustainable, and any further operations will trigger an OOM exception, which is critical to catch before the training job is unexpectedly killed by the scheduler.
- ✗
The learning rate is too low for the current hardware.
Why it's wrong here
Learning rate settings do not directly influence GPU memory usage in this manner. Memory consumption is driven by the model size, activation buffers, and batch size, not the optimizer step size. Lowering the learning rate would not resolve the memory pressure shown in the logs, which requires architectural or batch-size adjustments.
- ✗
GPU utilization is too low for efficient training.
Why it's wrong here
GPU utilization is consistently high at 98-99%, indicating that the hardware is being used very effectively to process data. The issue is not underutilization, but rather the exhaustion of the available memory buffer, which is a distinct problem that limits the maximum batch size the system can sustain.
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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 NCA-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 NCA-GENL exam.