Technical Stack and Infrastructure Questions
Addresses the technology stack and platform infrastructure choices that support a product. Topics include cloud architecture, deployment platforms, service and data layer design, observability and monitoring, scalability and performance considerations, tooling for build and release, and assessing technical debt or modernization needs in the stack. Also covers evaluation of BI tools, ETL platforms, and how infrastructure choices affect team velocity and operational cost.
HardTechnical
0 practiced
Your company's cloud analytics bill has ballooned and leadership asks you to reduce costs by 40% within three months without materially impacting critical reporting. Create a 3-month action plan with immediate quick wins, medium-term optimizations, and longer-term architectural changes, and propose KPIs to track progress and validate no business impact.
EasyTechnical
0 practiced
List and explain five key observability metrics a BI team should monitor for a production reporting platform (dashboards and data pipelines). For each metric describe a reasonable alert threshold and why it matters to business users and stakeholders.
EasyTechnical
0 practiced
Define three concrete Service Level Indicators (SLIs) and matching Service Level Objectives (SLOs) appropriate for a BI dashboard platform (for example: dashboard availability, 95th percentile query latency, and data freshness). Explain how each SLI maps to business impact and suggest measurement approaches.
MediumTechnical
0 practiced
Given a table events(event_id UUID, user_id INT, event_type VARCHAR, occurred_at TIMESTAMP), write an ANSI SQL query (compatible with Postgres or BigQuery) to compute daily active users (DAU) for the past 30 days and a 7-day retention cohort table showing the percentage of users who return on day 1, day 7, and day 14 after their first event in the 30-day window.
HardTechnical
0 practiced
Explain strategies to optimize costs for cloud data warehouses such as Snowflake or BigQuery. Discuss partitioning/clustering, materialized views, storage vs compute trade-offs, reducing scanned bytes via query optimization, and governance controls like resource monitors and cost alerts.
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