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.

MediumTechnical

0 practiced

You are tasked with creating a nightly pipeline to compute hundreds of aggregates. Suggest orchestration and dependency patterns to ensure correct ordering, parallelism, and efficient retries. Consider DAG design, idempotence, and checkpointing.

EasyTechnical

0 practiced

Write an ANSI SQL query to compute month-over-month percentage growth of active users from an events table. Given the table schema below, produce columns year_month (YYYY-MM), active_users, mom_growth_pct. Handle months with zero baseline gracefully and use window functions. Explain any assumptions about partial months and time zones.

events(
  user_id int,
  occurred_at timestamp,
  event_type varchar
)

MediumSystem Design

0 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.

MediumTechnical

0 practiced

Compare batch vs streaming architectures for near-real-time dashboards. Discuss latency, cost, complexity, data completeness, idempotence, and operational overhead. Provide guidance for choosing one over the other for a metrics dashboard with 1-minute freshness SLA.

MediumTechnical

0 practiced

Given a slow query that joins a large orders table to products and aggregates revenue, rewrite it to be more efficient. Assume schema:

orders(order_id int, product_id int, amount decimal, order_ts timestamp)
products(product_id int, category varchar)

Explain indexing, partitioning, and aggregation strategies you would apply.

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