NCA-GENL Data Analysis and Visualization Practice Question
You are performing a comparative analysis of two different LLM architectures by visualizing their performance on a RAG (Retrieval-Augmented Generation) benchmark. Which visualization is best for comparing the distributions of answer accuracy scores?
⚠ Common exam trap
Test-takers frequently select scatter plots or line charts, failing to realize that box plots are ideal for comparing distributions, medians, and outliers across multiple models.
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
✓
Box plot comparing accuracy scores.
Box plots (or box-and-whisker plots) provide a compact summary of data distribution, including median, quartiles, and outliers. When comparing two architectures, they allow for an immediate visual assessment of consistency, range, and bias. This is crucial in RAG benchmarking because high accuracy is insufficient; developers need models that consistently perform well across diverse queries, and box plots reveal whether one architecture suffers from more frequent low-quality outliers than the other.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Stacked bar chart of token counts.
Why it's wrong here
Stacked bar charts are used to visualize compositions and categorical relationships, not the distribution of accuracy scores. They cannot show the spread, median, or outliers of performance data, making them entirely inappropriate for comparing the reliability or accuracy distributions of two different LLM architectures in a benchmark study.
- ✓
Box plot comparing accuracy scores.
Why this is correct
Box plots are ideal for comparing statistical distributions. They highlight the median performance and the spread of scores, allowing developers to immediately identify which model has a more consistent performance profile and which one is prone to extreme outliers, which is essential for benchmarking different LLM performance architectures.
- ✗
Radial plot of training time.
Why it's wrong here
Radial plots are typically used for displaying multivariate data or benchmarking across multiple independent criteria in a spider-web format. They do not effectively display the distribution of accuracy scores across a dataset, making them poor tools for comparing the performance consistency of two different model architectures during evaluation.
- ✗
Individual data point scatter plot.
Why it's wrong here
While scatter plots show individual results, they become cluttered and difficult to interpret when comparing large datasets. Without statistical summary markers, they fail to provide the immediate distributional insight that box plots offer, making it harder to discern the overall performance trend between the two competing LLM architectures.
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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
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