Create graphs, charts, or other visualizations to convey the results of data analysis using specialized software.

## Scenario **Difficulty Level:** Applied You are a machine learning engineer preparing a quarterly review for executive stakeholders who will decide whether to expand your team's generative AI pilot program. Over the past three months, you have collected performance metrics on three candidate LLMs: response latency percentiles, token costs per 1,000 requests, and bias detection scores across six categories. Your manager has asked you to present the findings in a format that enables non-technical decision-makers to compare the models quickly and understand trade-offs between cost, speed, and fairness[^1][^2]. You have access to Python visualization libraries, cloud-based dashboards, and spreadsheet tools that can produce **histograms**, **bar charts**, and radar charts[^4][^6]. However, you know that poorly chosen visualizations can obscure patterns or introduce cognitive load that prevents stakeholders from grasping key differences[^8]. **What would you do, and why?** Which visualization types would you select for each metric category, and how would you justify your choices to ensure transparency and support decision-making[^4]?