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.
MediumTechnical
0 practiced
A product analytics dashboard needs near-real-time metrics for user activity and accurate daily aggregates for finance. Design an ETL/data pipeline: choose between batch, micro-batch, and streaming, and justify trade-offs in latency, cost, complexity, and consistency. Propose a hybrid approach if appropriate.
EasyTechnical
0 practiced
You're building an executive sales dashboard and find NULLs in monthly revenue for some stores. Describe steps to diagnose the root cause and propose algorithmic approaches to handle missing numerical values for reporting (e.g., zero-fill, forward-fill, interpolation). For each approach, include pros/cons and when it is appropriate.
EasyTechnical
0 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.
MediumTechnical
0 practiced
List and explain five common SQL anti-patterns that cause slow BI queries (e.g., SELECT *, applying functions to indexed columns, unbounded CROSS JOINs). For each anti-pattern propose a practical fix and how you would measure the improvement in performance.
MediumTechnical
0 practiced
Implement a Python function that returns the k most frequent items from a list of strings. Assume the list fits in memory. Aim for O(n log k) time and O(k) extra space (excluding the frequency map). Provide code and explain complexity and edge cases.
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