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Coding questions
Explore algorithm tutorials, coding and system design questions, interview experiences, and computer science study guides.
1,899 resources · Page 15 of 80
Coding questions · Intuit
Compute monthly signups, conversion, and YoY growth
This question evaluates a candidate's ability to perform data manipulation and time-series aggregation to compute monthly signups, subscription conversion rates, and year-over-year growth from user-level tables using SQL or Python (Data Manipulation (SQL/Python)).
Coding questions · Microsoft
Compute most popular location with weights
This question evaluates a candidate's ability to perform weighted aggregation, data cleaning, tie-breaking logic, and handling of malformed or missing text fields using SQL and/or Python.
Coding questions · Atlassian
Compute moving average over last N stream
This question evaluates understanding of streaming algorithms and sliding-window aggregation within the Coding & Algorithms domain, focusing on incremental computation, state management, and numerical stability when computing moving averages.
Coding questions · Meta
Compute multi-account actives and unread coverage
This question evaluates proficiency in SQL data manipulation and analytics, including per-person aggregation across multiple accounts, deduplication, date-based filtering, join logic, and cohort-level percentage calculations.
Coding questions · Meta
Compute multi-account activity and unread percentages in SQL
This question evaluates SQL-based data manipulation and analytics skills, including joining daily snapshots to event tables, applying time-window filters, mapping multiple accounts to a person, and computing aggregate counts and unread percentages.
Coding questions · Meta
Compute multi-account user distribution and unread pct
This question evaluates a data scientist's ability to perform relational data manipulation and aggregation with SQL or Python, covering competencies such as handling one-to-many joins, grouping and bucketing counts, deduplicating across related tables, and computing cohort-level percentages from boolean flags.
Coding questions · DoorDash
Compute nearest courier for each customer
This interview question evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer for Compute nearest courier for each customer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Coding questions · DoorDash
Compute nearest dashmart and busiest assignments
Compute nearest dashmart and busiest assignments evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Coding questions · DoorDash
Compute nearest dashmart distances for queries
This question evaluates a candidate's understanding of graph traversal and shortest-path computation on grids, spatial reasoning about obstacles and sources, and the ability to analyze algorithmic scalability and complexity when answering many queries.
Coding questions · DoorDash
Compute nearest-exit distances in a grid
Compute nearest-exit distances in a grid evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Coding questions · Tesla
Compute nearest index within threshold after walking distances
This question evaluates a candidate's proficiency in data manipulation, numerical computing with NumPy, spatial reasoning for Euclidean distance and interpolation along polylines, and designing time- and space-efficient algorithms for large-scale inputs.
Coding questions · Meta
Compute nested depth sum and grid distance
This pair of problems evaluates algorithmic proficiency with nested data structures and grid-based graph traversal, covering concepts such as depth-weighted aggregation and shortest-path computation under obstacles.
Coding questions · Meta
Compute nested depth-weighted sum
This question evaluates understanding of tree-like nested data structures, traversal strategies (recursive DFS and iterative approaches), handling of edge cases such as empty lists and negative integers, and algorithmic reasoning about time and space complexity within the Coding & Algorithms domain.
Coding questions · Amazon
Compute optimal locker placement with obstacles
This interview question evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer for Compute optimal locker placement with obstacles states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Coding questions · XPeng
Compute optimal matrix multiplication order
This question evaluates a candidate's understanding of dynamic programming, optimal substructure, and algorithmic optimization for matrix chain multiplication, including reconstructing an optimal parenthesization and adapting to variant cost models.
Coding questions · Uber
Compute outer boundary of an N-ary tree
This question evaluates proficiency in tree data structures and algorithm design, focusing on traversal and boundary extraction in ordered N-ary trees while requiring time and space complexity analysis.
Coding questions · Intuit
Compute paid subscriber YoY counts by month
This question evaluates SQL-based data manipulation skills including calendar-month aggregation, year-over-year comparisons, anti-joins to exclude records, NULL handling, and query performance considerations on large tables.
Coding questions · Rippling
Compute peak busy dashers with overlaps
This question evaluates skills in interval-overlap reasoning, sweep-line algorithm design, event creation and deduplication, comparator and tie-breaking subtleties, and asymptotic time/space complexity analysis within the Coding & Algorithms domain.
Coding questions · Rippling
Compute peak concurrent drivers in 24 hours
This question evaluates the ability to design algorithms and data structures for streaming interval data, specifically computing peak distinct-driver concurrency over a half-open 24-hour time window with attention to boundary inclusivity and distinct-count semantics.
Coding questions · Amazon
Compute peak parking lot occupancy intervals
This question evaluates a candidate's competency in temporal interval reasoning, event-stream processing, and handling incomplete or unsorted logs to determine peak concurrent occupancy.
Coding questions · Others
Compute per-coin quarterly amounts and totals
This question evaluates competence in SQL data manipulation—specifically time-based conditional aggregation, grouping, joins, and handling entities with no activity—to compute per-coin quarterly sums and year-to-date totals.
Coding questions · Tencent
Compute per-player daily cumulative games with windows
This question evaluates proficiency with SQL window functions, aggregation, date-aware grouping and joins for computing per-player daily counts and running totals, and it falls under Data Manipulation (SQL/Python) for Data Scientist roles, emphasizing practical application rather than purely theoretical concepts.
Coding questions · Meta
Compute percent of active users with 50+ calls
This question evaluates a data scientist's competence in Data Manipulation (SQL/Python), focusing on SQL aggregation, joining call and user snapshot tables, temporal filtering, and computing user-level engagement metrics across incoming and outgoing call records.
Coding questions · Pinterest
Compute percent of first-cancelled users who never rebook
This question evaluates proficiency in SQL data manipulation—specifically use of window functions, CTEs, deduplication by earliest timestamp, temporal comparisons, and cohort-level aggregation—within the Data Manipulation (SQL/Python) domain for a Data Analyst role in health‑tech product analytics.