NCA-GENL Data Analysis and Visualization Practice Question
You are monitoring a production LLM inference service on an NVIDIA GPU. The service's request latency distribution is heavily right-skewed, and a small fraction of requests take far longer than the rest. You need a visualization that shows the full distribution shape, including the median and the extreme tail, to decide whether the GPU is under-provisioned. Which visualization should you use?
⚠ Common exam trap
The trap here is assuming a mean latency line is sufficient, when a skewed distribution's tail is exactly what a mean obscures.
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 box plot of request latency with whiskers extending to the 1.5 IQR and outliers plotted individually.
Because the latency distribution is right-skewed, summary statistics like the mean hide the tail. A box plot exposes the median, quartiles, and individual outliers, so the extreme slow requests remain visible. This lets you decide whether the long tail is severe enough to justify additional GPU capacity.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
A box plot of request latency with whiskers extending to the 1.5 IQR and outliers plotted individually.
Why this is correct
A box plot directly shows the median, interquartile range, and individual outliers beyond the whiskers, which is exactly the tail behavior you need. It summarizes the skewed latency distribution compactly and makes the extreme slow requests visible instead of hiding them behind a single average value.
- ✗
A single line chart of mean request latency sampled every minute.
Why it's wrong here
A mean latency line collapses the skewed distribution into one number per interval, so the extreme tail requests disappear into the average. It cannot show the median or the shape of the distribution, which is the specific evidence you need to judge whether slow outliers justify more GPU capacity.
- ✗
A pie chart showing the proportion of requests in each latency bucket.
Why it's wrong here
A pie chart shows proportions of a whole but not the magnitude or spread of latency values, and it becomes unreadable with many buckets. It also cannot reveal the median or the extreme tail requests that are driving the capacity concern.
- ✗
A scatter plot of GPU utilization against time for the last 24 hours.
Why it's wrong here
GPU utilization over time shows resource usage, not the latency distribution across requests. It cannot expose the median, quartiles, or extreme tail of request latency, so it does not answer whether a small fraction of slow requests indicates under-provisioning.
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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.