Data-Centric Algorithmic Problem Solving Questions
Foundational algorithm design and data-structure concepts with an emphasis on data-centric problem solving. Covers algorithmic paradigms (e.g., greedy, dynamic programming, divide-and-conquer, graph algorithms), data structures, complexity analysis, and practical approaches to solving computational problems using data.
EasyTechnical
54 practiced
Given the events table schema below (ANSI SQL), write a query to compute Monthly Active Users (MAU) for each month in 2024. MAU = count of distinct user_id with at least one event in that month. Return columns: month, mau and order results by month.| event_id | user_id | event_type | occurred_at ||----------|---------|------------|-------------|
MediumTechnical
36 practiced
Describe t-digest or a similar algorithm for approximate quantile estimation on large datasets. Explain how it summarizes data, merges summaries across partitions, error characteristics (especially at tails), and when you'd prefer it over exact sort-based methods in BI reporting.
MediumTechnical
32 practiced
How would you implement stratified random sampling from a very large distributed dataset when group (stratum) sizes are unknown in advance? Describe a scalable algorithm that ensures representativeness across strata, and discuss trade-offs between one-pass and two-pass approaches in Spark/MapReduce.
EasyTechnical
32 practiced
What are SQL window functions and how do they differ from GROUP BY aggregations? Give two BI examples: calculating rank per cohort and computing percent change versus previous period, and indicate which OVER clauses you'd use.
EasyTechnical
44 practiced
Given a CSV file sales.csv with columns (store_id, date, amount), write Python (pandas) code to compute total sales per store per month. Include a brief note on how pandas groups and aggregates and how it leverages hashing under the hood for groupby-like operations.
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