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.
HardTechnical
0 practiced
Design an algorithm to produce a stratified sample from a distributed dataset that enforces minimum representation for protected groups (e.g., at least 100 samples per group) while preserving randomness. Discuss how to enforce such quotas when group sizes are small or unknown and how to implement in Spark or MapReduce.
MediumTechnical
0 practiced
Write Python code to merge two sorted iterators (generators yielding numbers in ascending order) into a single sorted iterator without loading all elements into memory. The solution should handle infinite/very large streams and perform lazy consumption.
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.
HardTechnical
0 practiced
You propose replacing exact daily unique user counts with HyperLogLog approximations to save storage and compute. Executives are concerned about accuracy for billing and targets. How would you present trade-offs, define acceptable error budgets, validate approximations, design a rollout plan (A/B or shadow mode), and build guardrails to maintain stakeholder trust?
HardTechnical
0 practiced
You need to compute daily unique user counts (distinct user_id) for many websites with billions of events. Compare exact distinct counting to probabilistic methods like HyperLogLog (HLL): explain memory footprint, error behavior, merging, how to pick HLL parameters, and methods to validate HLL in production.
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