Dashboard and Data Visualization Design Questions

Principles and practices for designing, prototyping, and implementing visual artifacts and interactive dashboards that surface insights and support decision making. Topics include information architecture and layout, chart and visual encoding selection for comparisons trends distributions and relationships, annotation and labeling, effective use of color and white space, and trade offs between overview and detail. The topic covers interactive patterns such as filters drill downs tooltips and bookmarks and decision frameworks for when interactivity adds user value versus complexity. It also encompasses translating analytic questions into metrics grouping related measures, wireframing and prototyping, performance and data latency considerations for large data sets, accessibility and mobile responsiveness, data integrity and maintenance, and how statistical concepts such as statistical significance confidence intervals and effect sizes influence visualization choices.

EasyTechnical

0 practiced

When you observe an anomalous spike in a KPI on a dashboard, how would you annotate and label that event so users understand cause and impact? Discuss when to use inline annotations, timeline markers, links to drill-through analysis, and how to avoid cluttering the view with too many annotations.

MediumTechnical

0 practiced

Design a responsive dashboard layout for field sales managers on mobile devices. Identify which components to surface first, how to reorder and collapse content, how to handle filters and actions for touch, and strategies for dealing with intermittent connectivity and offline access.

MediumTechnical

0 practiced

You have a Postgres orders table described as orders(order_id PK, user_id, occurred_at TIMESTAMP, region VARCHAR, amount DECIMAL) with about 100M rows. Write an optimized SQL query to compute daily unique active users for the last 30 days per region. Explain index recommendations, partitioning approach, and how to avoid full table scans.

HardSystem Design

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You are asked to support near-real-time dashboards with high-cardinality dimensions such as per-user metrics. Evaluate streaming vs micro-batch architectures, summarization strategies (sketches, approximate counts, rollups), and discuss trade-offs in cost, latency, consistency, and query complexity.

MediumTechnical

0 practiced

Given an A/B test dataset containing variant, date, conversions, and sample size, design visualizations to show conversion rate per variant, cumulative lift over time, p-values or confidence intervals, and a decision rule for stopping the test. Explain how to annotate the dashboard to prevent misinterpretation.

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