NCA-GENL Data Analysis and Visualization Practice Question
A data scientist is monitoring a fine-tuning job on a DGX system. The training loss graph shows a sharp, localized spike followed by an immediate return to the previous trend. What is the most likely cause?
⚠ Common exam trap
Candidates often assume the model is failing or the learning rate is too high, missing the fact that a single, sharp, transient spike usually indicates a localized data quality issue.
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
✓
A single corrupted data sample was processed.
Spikes in training loss often indicate transient data quality issues or hardware-level hiccups, such as a localized bit-flip or a corrupt sample in a data shard. Identifying these outliers is critical in large-scale model training to prevent convergence issues or model degradation. By isolating the cause, researchers can decide whether to skip the sample or investigate infrastructure stability, ensuring the model weight updates remain numerically stable and representative of the intended training distribution.
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 has reached global convergence prematurely.
Why it's wrong here
Global convergence is characterized by a stable, low plateau in loss values rather than a transient spike. A sharp increase suggests numerical instability or a faulty data sample, whereas convergence implies that the optimizer has successfully minimized the objective function across the entire training dataset without further significant improvements.
- ✗
The learning rate is set significantly too high.
Why it's wrong here
An excessively high learning rate typically results in sustained divergence or a catastrophic increase in loss that never returns to previous levels. A transient spike followed by recovery points toward a single anomalous input or a temporary system-level glitch rather than a persistent hyperparameter configuration error across the entire batch.
- ✓
A single corrupted data sample was processed.
Why this is correct
A corrupted sample or an outlier that violates the expected data distribution often causes a sudden, momentary spike in the gradient calculation. Once that batch is processed and the optimizer proceeds to the next valid data point, the loss typically returns to its previous trend as the model resumes learning.
- ✗
The GPU memory buffer has overflowed.
Why it's wrong here
GPU memory overflow errors typically result in immediate program termination or a CUDA exception, not a transient spike in training loss. If memory were insufficient, the job would fail to allocate necessary buffers, preventing the model from performing the forward and backward passes required to generate any loss metrics.
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 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.