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Interview Questions
Practice 9403 coding, system design and machine learning questions from 546 companies, sorted by role, topic and interview round. Every question has step-by-step hints, a code runner that supports 8 languages and an AI mentor; members also get the full solution walkthrough.
- 9403
- Questions
- 546
- Companies
- 11
- Topics
- 8
- Languages
Showing 4021–4050 of 4616 results
- TikTokMediumData ScientistCalculate Day-7 Retention Rate from User Post Data
This question evaluates a candidate's competency in SQL and Python data wrangling, emphasizing date arithmetic, window functions, grouping, and conditional aggregation for cohort and retention calculations.
Data Manipulation (SQL/Python)Technical Screen - TikTokEasyData ScientistCalculate Expected Draws for X > 0.8 in Uniform(0,1)
This interview question evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer for Calculate Expected Draws for X > 0.8 in Uniform(0,1) states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Statistics & MathTechnical Screen - TikTokMediumData ScientistCalculate User Registration Date and 7-Day Retention Rate
This question evaluates the ability to perform time-based data manipulation and cohort retention analysis using SQL/Python, including deriving registration dates, aggregating posts within time windows, and computing retention metrics.
Data Manipulation (SQL/Python)Technical Screen - TikTokMediumData ScientistCalculate valid daily usage with gap constraints
This question evaluates a candidate's ability to manipulate temporal intervals, including clipping to a specified day window, merging touching or overlapping segments, detecting gaps, and aggregating minute-level durations using SQL window functions and date/time arithmetic, and belongs to the Data Manipulation (SQL/Python) domain.
Data Manipulation (SQL/Python)Technical Screen - TikTokMediumData ScientistCompare Random Forests and Boosted Trees: Bias, Variance, Speed
Evaluates practical trade-offs between Random Forests and gradient-boosted trees for tabular ML. Strong answers compare bias, variance, speed, interpretability, overfitting, production fit, and feature scaling needs.
Machine LearningTechnical Screen - TikTokMediumData ScientistCompute 7-day rolling complaint/order ratio in SQL
This question evaluates SQL data manipulation skills, specifically time-based aggregations, sliding window functions, joins to include entities with no events, and numeric casting within the Data Manipulation (SQL/Python) domain.
Data Manipulation (SQL/Python)Technical Screen - TikTokMediumData ScientistCompute and rank top bad advertisers
This question evaluates proficiency in SQL data manipulation and analytics, including time-window filtering, JOINs, aggregation and deduplication, rate calculations, and window-function–based ranking.
Data Manipulation (SQL/Python)Technical Screen - TikTokMediumMachine Learning EngineerCompute city skyline silhouette
Compute city skyline silhouette 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 & AlgorithmsTechnical Screen - TikTokMediumData ScientistCompute last-to-previous ad impression gaps
This question evaluates proficiency in data manipulation and time-based analytics, covering SQL window functions, timestamp arithmetic, and grouping/ordering semantics within the Data Manipulation (SQL/Python) domain.
Data Manipulation (SQL/Python)Technical Screen - TikTokMediumSoftware EngineerCompute length of longest increasing subsequence
This question evaluates understanding of subsequences, dynamic programming and algorithmic optimization for array processing, and is categorized under Coding & Algorithms; it emphasizes practical application of algorithm design and complexity analysis rather than purely theoretical knowledge.
Coding & AlgorithmsTechnical Screen - TikTokMediumMachine Learning EngineerCompute minimum path sum in a triangle
This problem tests your ability to optimize path calculations in a non-linear data structure using dynamic programming, specifically focusing on space complexity optimization.
Coding & AlgorithmsTechnical ScreenDynamic ProgrammingArraysSpace Optimization - TikTokMediumSoftware EngineerCompute pipeline order from dependencies
This question evaluates understanding of graph algorithms and dependency resolution, specifically topological sorting and cycle detection, along with competence in choosing appropriate data structures and analyzing time/space complexity.
Coding & AlgorithmsTechnical Screen - TikTokMediumSoftware EngineerCount deletions making array fair
This question evaluates proficiency with array manipulation, parity-aware summation, and combinatorial counting when elements are removed, situating it in the Coding & Algorithms domain.
Coding & AlgorithmsTechnical Screen - TikTokMediumSoftware EngineerCount distinct island shapes in a grid
This problem tests grid traversal (DFS/BFS) and the ability to represent spatial structures in a canonical form to identify unique patterns.
Coding & AlgorithmsTechnical ScreenDFSGraphGrid - TikTokMediumSoftware EngineerCount islands on a torus grid
This question evaluates skills in graph and grid algorithms, topology-aware adjacency (torus wrap-around), and maintaining dynamic connectivity under incremental updates.
Coding & AlgorithmsTechnical Screen - TikTokMediumMachine Learning EngineerCount subarrays summing to target
This problem tests your ability to optimize subarray sum calculations from an O(n^2) approach to O(n) using a hash map for prefix sum tracking.
Coding & AlgorithmsTechnical ScreenArraysHash MapPrefix Sum - TikTokMediumData EngineerDebug a Hive Query for DAU
This question evaluates proficiency in Hive/SQL query formulation, data partitioning and pruning, join and aggregation semantics, timestamp handling, and performance tuning for large-scale analytics.
Data Manipulation (SQL/Python)Technical Screen - TikTokMediumData EngineerDebug a Hive insert query
This question evaluates a candidate's ability to debug Hive INSERT/SELECT operations, focusing on schema and column alignment, data types and casts, partition handling, file formats/SerDe, ACID/transactional constraints, and syntax or reserved keyword issues.
Data Manipulation (SQL/Python)Technical Screen - TikTokHardMachine Learning EngineerDefine QKV for recommender cross-attention
This question evaluates understanding of Transformer-style cross-attention and the concrete design of Query, Key, and Value tensors for deep-learning recommender systems, testing representation semantics, embedding alignment, and interaction modeling between user history, candidate items, and context.
Machine LearningTechnical Screen - TikTokMediumSoftware EngineerDelete nodes in linked list and binary tree
This question evaluates proficiency in mutable data structure operations, specifically linked list node removal and binary tree node replacement/deletion, requiring knowledge of traversal, pointer/reference manipulation, and edge-case handling.
Coding & AlgorithmsTechnical Screen - TikTokMediumData ScientistDerive L1 vs L2 effects with correlation
This question evaluates understanding of regularization methods (ridge/L2, LASSO/L1, and elastic net), multicollinearity effects, geometric intuition of constraint sets, and hyperparameter influence on coefficient patterns in the Statistics & Math domain.
Statistics & MathTechnical Screen - TikTokMediumData ScientistDescribe Your Most Impactful Project Experience and Lessons Learned
This interview question evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer for Describe Your Most Impactful Project Experience and Lessons Learned states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Behavioral & LeadershipTechnical Screen - TikTokMediumData EngineerDescribe a data-heavy project
Describe a data-heavy project evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Behavioral & LeadershipTechnical Screen - TikTokMediumSoftware EngineerDescribe a project you are proud of
This question evaluates technical leadership, ownership, system design, execution, collaboration, trade-off reasoning, and the ability to demonstrate measurable impact from a software project.
Behavioral & LeadershipTechnical Screen - TikTokMediumSoftware EngineerDescribe a service performance challenge
This question evaluates competency in production performance engineering, observability-driven diagnosis, experimentation and impact quantification, as well as leadership in prioritizing trade-offs and risk mitigation.
Behavioral & LeadershipTechnical Screen - TikTokMediumSoftware EngineerDescribe career plan and teamwork approach
This question evaluates a candidate's career planning, self-awareness, and teamwork competencies, specifically goal setting, alignment with team priorities, conflict resolution, and collaborative decision-making.
Behavioral & LeadershipTechnical Screen - TikTokMediumMachine Learning EngineerDescribe internship and research projects
This interview question evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer for Describe internship and research projects states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Behavioral & LeadershipTechnical Screen - TikTokMediumSoftware EngineerDescribe toughest challenge and resolution
This question evaluates a candidate's problem-solving, leadership, communication, decision-making, and ability to quantify impact when resolving complex technical or organizational challenges.
Behavioral & LeadershipTechnical Screen - TikTokMediumSoftware EngineerDesign LRU cache with O(1) operations
This question evaluates a candidate's understanding of data structures and algorithm design, specifically skills in implementing constant-time cache operations, eviction policies, update handling, and concurrency/thread-safety considerations.
Coding & AlgorithmsTechnical Screen - TikTokMediumData ScientistDesign Metrics for Content Moderation and Chatbot Evaluation
This interview question evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer for Design Metrics for Content Moderation and Chatbot Evaluation states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationTechnical Screen
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