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

Explain annotation and labeling best practices for dashboards: axis labels, concise titles, units, inline labels vs tooltips, callouts for anomalies, and including metadata such as data source and last refresh. Provide a short example annotation for a sudden revenue spike in March.

MediumTechnical

0 practiced

Your dashboard's monthly revenue is 8% lower than finance's official report. Provide a systematic debugging checklist to identify and resolve the discrepancy: include source differences, aggregation logic, joins, timezone and currency issues, duplicate rows, late-arriving data, and cut-off timing. Explain the priority order of checks and quick checks to run first.

EasyTechnical

0 practiced

Write an ANSI SQL query to compute daily active users (DAU) for the last 30 days using the events table: events(event_id, user_id, event_type, event_timestamp). Return rows (date, dau) for each date in the 30-day window including dates with zero users. Assume event_timestamp is UTC. Also list indexes or partitions you'd recommend for a table with billions of rows.

HardTechnical

0 practiced

Design an end-to-end system to detect, score, and surface anomalies in key dashboard metrics (traffic, conversions, revenue). Specify pipelines for streaming and batch detection, scoring cadence, notification channels, UI affordances for flagged anomalies, feedback loops to reduce false positives, and SLAs for detection latency.

MediumSystem Design

0 practiced

Describe best practices to maintain dashboards at scale across an organization: naming conventions, documentation, version control, automated metric tests, ownership, and deprecation policy. Provide a sample lightweight governance process that balances agility and control.

Unlock Full Question Bank

Get access to hundreds of Dashboard and Data Visualization Design interview questions and detailed answers.

Join thousands of developers preparing for their dream job.