Automated Reporting & Report Development Questions
Build automated reports that refresh on schedule. Understand refresh schedules, data pipeline integration, and deployment to production. Create parameterized reports for different stakeholder needs. Know how to version control and manage report changes.
MediumTechnical
91 practiced
Provide a high-level Python Airflow DAG pseudocode that orchestrates: 1) an ETL job (dbt run), 2) a warehouse table materialization, 3) triggering a BI tool dataset refresh via API, and 4) sending a Slack alert on failure. Include retries, SLA monitoring, and a simple on-failure notification.
EasyTechnical
73 practiced
Explain row-level security (RLS) in the context of automated reports. Describe three implementation approaches (BI-tool RLS, warehouse view-based RLS, and middleware filtering), trade-offs for performance and maintainability, and how RLS impacts report caching and refresh strategies.
HardTechnical
84 practiced
An executive dashboard displays incorrect totals only during month-end processing windows. Describe a thorough troubleshooting plan: what hypotheses you'd form (e.g., late-arriving data, race conditions, partitioning lag), what logs/metrics to inspect, what temporary mitigations to apply, and what permanent fixes you'd implement.
HardTechnical
76 practiced
Design a multi-region automated reporting setup for low-latency dashboards in three continents with local data residency constraints. Discuss data replication/ETL strategies, consistency models, conflict resolution, and how to keep report automation (scheduling, versioning) consistent across regions while controlling cost.
MediumTechnical
69 practiced
Provide a LookML or pseudocode snippet that demonstrates parameterized date range (e.g., last_7_days, last_30_days, custom) and a top-N parameter for a dashboard. Explain how you'd test this functionality and ensure it is safe for scheduled automated reports.
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