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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 4141–4170 of 4616 results
- TikTokMediumSoftware EngineerWrite complex SQL for cohorts and retention
This question evaluates advanced SQL and data-engineering competencies such as cohort and retention analysis, deduplication, timezone normalization, last-touch attribution, aggregate revenue calculations, and effective use of window functions in the Data Manipulation (SQL/Python) domain.
Data Manipulation (SQL/Python)Technical Screen - TmlMediumSoftware EngineerImplement a Resizable Deque
This question evaluates proficiency in data structures and array-based implementations, specifically building a resizable double-ended queue (deque) backed by a circular array and understanding amortized O(1) operation costs.
Coding & AlgorithmsTechnical Screen - ToastHardSoftware EngineerCode Review of a Multi-File HTTP API: Wrong Method, Payload and Validation Bugs
A code-review exercise on several files that implement an HTTP API, where the known defects include a GET endpoint that should be POST, a request payload problem and missing input validation. It tests explaining each defect by its impact, fixing it consistently across files, and running a prioritized review within an hour.
Software Engineering FundamentalsTechnical Screen - ToastHardSoftware EngineerDesign a Restaurant Seat and Table Reservation System
A system design question asking for a restaurant seat reservation system in which diners search availability by party size and time, then book, change or cancel. It tests the table-and-time inventory model, preventing double booking under concurrent requests, holds, availability caching and partitioning by restaurant.
System DesignTechnical Screen - Together AIHardSoftware EngineerDesign a GPU-aware pod scheduler
This interview question evaluates requirements, scale assumptions, API/data design, architecture, trade-offs, failure modes, and rollout in a realistic interview setting. A strong answer for Design a GPU-aware pod scheduler states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
System DesignTechnical Screen - Together AIMediumSoftware EngineerDetect cycles and break them in pod dependencies
This question evaluates proficiency with directed graph concepts and dependency resolution, focusing on cycle detection and selecting a single dependency edge whose removal renders the graph acyclic.
Coding & AlgorithmsTechnical Screen - Together AIEasySoftware EngineerDiagnose CPU Performance Problems on a Linux Server
Diagnose Linux CPU slowness from host and thread metrics through scheduling, limits, profiles, and a workload-linked verification of the bottleneck.
Software Engineering FundamentalsTechnical Screen - Together AIEasySoftware EngineerDiagnose Slow Network Throughput Including Containers
Investigate slow network throughput across application, transport, host, and container layers using controlled comparisons, retransmissions, limits, and path behavior.
Software Engineering FundamentalsTechnical Screen - Together AIEasySoftware EngineerDiagnose Storage I/O Performance and Identify the Workload
Diagnose storage slowness using device metrics, process attribution, workload shape, cache and durability effects, and measurements appropriate to the storage stack.
Software Engineering FundamentalsTechnical Screen - Together AIMediumSoftware EngineerHistorical Lookups in a Timestamped Key-Value Store
Implement historical key-value lookups using timestamped writes, per-key ordered histories, and binary search for the latest eligible value.
Coding & AlgorithmsTechnical Screen - Together AIEasySoftware EngineerInvestigate a Server Saturated by Logging
Trace logging-related server saturation through resource attribution, producer/sink behavior, bounded queues, rotation and retention, and verified mitigation.
Software Engineering FundamentalsTechnical Screen - Together AIMediumSoftware EngineerSchedule GPU Pods and Drain a Node
Implement a GPU cluster manager to schedule workloads using a deterministic best-fit policy and simulate draining a node to check if all its pods can be migrated without disrupting other nodes.
Coding & AlgorithmsTechnical ScreenGreedySimulationSystem Design - Together AIEasySoftware EngineerSplit a Chunked Text Stream into Line-Balanced Parts
Construct any balanced whole-line partition of a chunked text stream, preserving exact content and newline rules while explaining buffering and streaming limits.
Coding & AlgorithmsTechnical Screen - TomaEasyFrontend EngineerApproximate Pi from Random Points
This question evaluates a candidate's understanding of Monte Carlo estimation, basic Euclidean geometry, probability, and numerical reasoning when using random sampling to approximate mathematical constants.
Coding & AlgorithmsTechnical Screen - TomaEasyFrontend EngineerIdentify the CSS color function
This question evaluates a candidate's understanding of CSS color functions, specifically knowledge of channel-based color specification and the use of an optional opacity component.
Software Engineering FundamentalsTechnical Screen - TomaMediumSoftware EngineerImplement AudioStream Mixer
This problem tests your ability to manage temporal data and stateful buffers by implementing a class that merges multiple incoming audio streams into a single output track using overwrite-based mixing logic.
Coding & AlgorithmsTechnical ScreenHashMapsData StructuresSimulation - TomaEasyFrontend EngineerWhich CSS function supports opacity?
This question tests your knowledge of modern CSS color syntax and the various ways to define transparency within style rules.
Software Engineering FundamentalsTechnical ScreenCSSFrontendStyling - Tools For HumanityEasyData ScientistCompute reply-based engagement metrics
This question evaluates proficiency in data manipulation and analytics, emphasizing SQL/Python skills for temporal filtering, joins between posts and users, deduplication, aggregation (distinct counts), and percentage calculations used to compute reply-based engagement metrics.
Data Manipulation (SQL/Python)Technical Screen - Tools For HumanityEasyData ScientistDetect and evaluate "stolen" posts
This question evaluates data-science competencies in instrumentation and feature design, content-similarity detection and labeling, causal inference for experiments, measurement of harms, and model evaluation, and belongs to the Analytics & Experimentation domain.
Analytics & ExperimentationTechnical Screen - TowerHardData ScientistDebug Oscillating GPU Utilization During Training
Design a systematic debugging and architectural remediation strategy for a distributed deep learning training cluster exhibiting severe GPU utilization oscillation between 90% and 20%.
ML System DesignTechnical Screendistributed-trainingml-system-designgpu-optimization - TowerHardData ScientistExplain Why Constant Neural-Network Initialization Fails
This question tests the conceptual understanding of weight initialization strategies and the symmetry-breaking property required for neural network gradient-based optimization.
Machine LearningTechnical ScreenDeep LearningInitializationOptimization - TowerMediumMachine Learning EngineerExplain the KV Cache and Prefill-Decode Disaggregation in LLM Serving
Explain how the KV cache speeds up autoregressive decoding in transformer language models, estimate how much GPU memory it needs, and describe prefill-decode disaggregation in LLM serving. It tests understanding of compute-bound versus memory-bound inference phases, cache sizing and the trade-offs of splitting the phases across GPU pools.
Machine LearningTechnical Screen - TransunionMediumData ScientistDescribe past project experience clearly
This prompt evaluates a candidate's competencies in applied data science tools and practices—including proficiency in Python or R, SQL querying and data transformation, problem scoping under ambiguity, technical-to-nontechnical communication, and team coordination.
Behavioral & LeadershipTechnical Screen - TransunionEasyData ScientistHow would you answer a DS video interview?
TransUnion's Data Scientist Intern technical screen is an asynchronous video interview with five ~2-minute prompts covering Python/R problem solving, SQL usage, working under ambiguity, explaining technical work to non-technical stakeholders, and keeping a team aligned. This guide gives a per-prompt structure, the evaluator rubric, strong sample outlines, and the common mistakes to avoid.
Behavioral & LeadershipTechnical Screen - TrexquantMediumSoftware EngineerC++ Fundamentals: const Pointers, and map vs unordered_map
This question evaluates a candidate's grasp of C++ fundamentals, specifically const-correctness with pointers and the distinction between ordered and hash-based associative containers. It tests conceptual understanding of pointer semantics and data structure trade-offs (tree vs. hash table complexity and ordering), a common check in language-fundamentals interview rounds.
Software Engineering FundamentalsTechnical Screen - TrexquantMediumSoftware EngineerStreaming Median Tracker
This question evaluates a candidate's ability to design a data structure that maintains a running median over a stream of numbers, testing knowledge of heaps and balanced data partitioning. It is a classic coding and algorithms interview problem used to assess understanding of amortized time complexity and efficient online statistics tracking, requiring practical implementation rather than just conceptual discussion.
Coding & AlgorithmsTechnical Screen - TripStackMediumSoftware EngineerCompute minimum resources for overlapping intervals
This question evaluates understanding of interval overlap reasoning, resource-allocation and scheduling concepts, and proficiency with algorithmic complexity and appropriate data structure choices.
Coding & AlgorithmsTechnical Screen - TripStackHardSoftware EngineerExplain Go concurrency and database choices
This question evaluates a candidate's understanding of Go concurrency primitives and scheduler behavior, cross-language concurrency comparisons, database model trade-offs (relational, document, columnar, key–value), and end-to-end storage design for high-concurrency systems.
System DesignTechnical Screen - TubiMediumData ScientistCompute daily net users from event logs
This question evaluates SQL-based data manipulation skills focused on daily aggregation, cohort and retention calculations, deduplicating event records, and grouping metrics by device_platform.
Data Manipulation (SQL/Python)Technical Screen - TubiHardData ScientistDetermine A/B test sample size drivers
This question evaluates statistical power and sample size determination for A/B testing, covering hypothesis testing for proportions, derivation using normal approximations, the impact of MDE, α and power choices, allocation ratios, cluster design (ICC and design effect), group‑sequential monitoring, and adjustment for overdispersed count outcomes.
Analytics & ExperimentationTechnical Screen
TikTok
Together AI
Trexquant
Tubi