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Business Intelligence and Reporting Infrastructure Questions

Building and operating reporting and business intelligence infrastructure that supports dashboards, automated reporting, and ad hoc analysis. Candidates should discuss data pipelines and extract transform load processes, data warehousing and schema choices, streaming versus batch reporting, latency and freshness trade offs for real time reporting, dashboard design for different audiences such as individual contributors managers and executives, visualization best practices, data validation and quality assurance, monitoring and alerting for reporting reliability, and governance concerns including access controls and privacy when exposing data.

MediumSystem Design
28 practiced
Design a monitoring and alerting plan for BI pipelines and dashboards. List key metrics to monitor (pipeline latency, row counts, backfills, dashboard render times), thresholds, alert channels, and on-call responsibilities. Explain how to avoid alert fatigue and false positives.
HardTechnical
34 practiced
You received two conflicting values for total revenue reported by two dashboards. Walk through a systematic reconciliation process to find the root cause: which tests, logs, and checks you run, and how you communicate findings to stakeholders.
MediumTechnical
32 practiced
Design a small experiment to validate whether a proposed dashboard improvement actually leads to faster decision making for managers. Define hypothesis, metrics to measure (qualitative and quantitative), sample selection, and success criteria.
EasyTechnical
32 practiced
Describe the difference between ETL and ELT in modern cloud data stacks. As a BI analyst, when would you recommend ETL vs ELT to a stakeholder considering data freshness, cost, transformations complexity, and compute location?
MediumTechnical
30 practiced
Write an ANSI SQL query to produce a simple 6-month cohort retention table using a purchases table. Schema:
purchases(user_id int, purchase_date date)
Output should show cohort_month and retention for months 0..5 as percentages of users in the cohort who purchased in subsequent months.

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