NCA-GENL Data Analysis and Visualization Practice Question
A data scientist is preparing a visualization to compare the performance of three different LLM fine-tuning runs on a summarization benchmark. They want to show both the central tendency and the variability of ROUGE-L scores across multiple evaluation samples. Which two visualizations are most appropriate for this goal? (Choose two.)
⚠ Common exam trap
The trap here is selecting a bar chart of averages or a pie chart of wins, which summarize outcomes but hide the sample-level variability that the question explicitly asks for.
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 ROUGE-L scores for each fine-tuning run.
To compare central tendency and variability of ROUGE-L scores across three runs, distributions must be shown per run. Box plots and violin plots both display median, quartiles, and spread, with violin plots adding density shape. The other options either collapse the score distribution, show only averages, or focus on a different relationship.
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 ROUGE-L scores for each fine-tuning run.
Why this is correct
A box plot displays the median, quartiles, and potential outliers for each run, directly showing central tendency and variability. It is ideal for comparing distributions across multiple groups. With three runs, side-by-side box plots make differences in median and spread immediately visible, which matches the requirement.
- ✗
A pie chart showing the proportion of samples where each run achieved the highest ROUGE-L score.
Why it's wrong here
A pie chart of win proportions shows relative frequency of being best, but it discards the actual ROUGE-L values and their variability. It cannot show central tendency or spread. While it answers a different question, it does not meet the stated goal of comparing score distributions across runs.
- ✗
A scatter plot of ROUGE-L versus ROUGE-1 for each sample, colored by run.
Why it's wrong here
A scatter plot of two different metrics shows their correlation, not the distribution of ROUGE-L for each run. While color can separate runs, the plot does not summarize central tendency or variability of ROUGE-L per run. It is better suited for metric correlation analysis, so it fails the stated goal.
- ✗
A single line chart plotting the average ROUGE-L score of each run over training epochs.
Why it's wrong here
A line chart of averages over epochs shows training dynamics but collapses the evaluation sample variability into a single mean per epoch. It does not show the distribution of ROUGE-L scores across samples, so it cannot convey variability. It addresses a different question about training progress, not sample-level distribution comparison.
- ✓
A violin plot of ROUGE-L scores for each fine-tuning run.
Why this is correct
A violin plot combines a box plot with a kernel density estimate, showing both summary statistics and the full shape of the distribution. It reveals multimodality and skewness that a box plot alone might hide. For comparing central tendency and variability across three runs, it is a strong choice.
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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.