Dashboard Structure & Actionability Questions
Learn to design dashboards that support decision-making. Organize metrics by user role: executives care about North Star and business outcomes; team leads care about specific channel or feature metrics. Include trends, comparisons, and targets. Practice explaining the purpose of each dashboard tier and how it enables specific decisions.
HardSystem Design
0 practiced
Design a CI/CD pipeline for dashboard deployment across dev, staging, and production environments. The pipeline must include automated metric tests to detect regressions, visual diffs for layout changes, permission checks, and rollback capabilities. Describe the types of tests (data tests, unit tests, visual tests), tools/integrations you would use, and how you'd measure pipeline effectiveness.
EasyTechnical
0 practiced
When building an executive dashboard meant to show high-level trends versus targets, which visualization types and layout patterns work best? Provide guidance on chart types (for example: KPI tiles, sparklines, bullet charts, small-multiples), aggregation levels, recommended use of color to indicate status, and how to present variance from target and trend direction in a single glance.
MediumSystem Design
0 practiced
Design a funnel analysis dashboard to identify drop-off points in a multi-step user journey: Landing → Signup → Onboard → Purchase. Specify required panels (overview funnel, step-level conversion by segment, historical trend, top drop-off cohorts), segmentation controls (device, cohort, source), the funnel conversion calculations, and how you would enable analysts to isolate causes with event-level traces.
MediumTechnical
0 practiced
Using PostgreSQL, write a query to produce weekly retention cohorts. Given table events(user_id, event_name, occurred_at TIMESTAMP), compute for each cohort week (the week of the user's first event truncated to week starting Monday UTC) the percent of users active in weeks 0 through 8 after cohort. Output columns: cohort_week, week_number, users_in_cohort, retained_users, retention_rate. Explain approach and any performance considerations.
MediumTechnical
0 practiced
You need near-real-time dashboards with <=1 minute latency that allow filtering by high-cardinality dimensions such as user_id and campaign_id. Propose a cost/latency-balanced architecture covering data ingestion, streaming or micro-batch processing, serving layer choices, pre-aggregations, and approximate algorithms (for example: HyperLogLog) if applicable. Discuss trade-offs and when approximations are acceptable.
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