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
0 practiced
Describe hash table collision-resolution strategies (separate chaining vs open addressing). For each approach explain memory trade-offs, expected lookup/insert/delete complexities, and when a data scientist would prefer one over the other for high-throughput lookups.
MediumTechnical
0 practiced
Explain Count-Min Sketch: structure, how to estimate frequency, how to choose width and depth given desired error and confidence, and limitations (overestimation, no deletions without variants). Describe a real-world scenario where you'd use it for log analytics.
EasyTechnical
0 practiced
Write a SQL query (Postgres syntax) to compute a 7-day rolling average of "events" per user. Given table schema: events(user_id INT, occurred_at DATE, event_count INT)Return columns: user_id, occurred_at, rolling_7d_avg. Explain assumptions about missing dates and how your query handles sparse dates.
HardTechnical
0 practiced
Outline an algorithmic approach for real-time anomaly detection on high-dimensional streaming telemetry. Cover feature extraction, dimensionality reduction, online model choices (e.g., incremental PCA, streaming isolation forest), concept drift detection, and how to evaluate alarms in production.
MediumSystem Design
0 practiced
You must compute item co-occurrence counts (pairwise counts) across user sessions for market-basket analysis using MapReduce. Describe a MapReduce job design that deals with combinatorial explosion and skew (popular items). Include combiner usage, thresholding, and sketch ideas for memory optimization.
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