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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.
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- 546
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- 11
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Showing 1471–1500 of 4616 results
- DoorDashHardData ScientistInvestigate Falling Successful Orders
This question evaluates a data scientist's ability to diagnose a drop in a product metric through anomaly verification, data validity checks, funnel and cohort analysis, causal attribution across marketplace sides (customer, restaurant, delivery), and experiment design.
Analytics & ExperimentationTechnical Screen - DoorDashHardData ScientistInvestigate Falling Successful Orders in LA
This problem tests your ability to decompose a drop in a high-level business metric into granular segments, formulate testable hypotheses, and propose methodologies to identify the root cause.
Analytics & ExperimentationTechnical ScreenRoot Cause AnalysisFunnel AnalyticsMarketplace Dynamics - DoorDashMediumData ScientistInvestigate LA Order Drop
This question evaluates marketplace analytics, causal inference, hypothesis-driven investigation, funnel decomposition, and experimentation design skills in a data science context.
Analytics & ExperimentationTechnical Screen - DoorDashEasyProduct AnalystInvestigate LA successful orders drop
This question tests your ability to methodically decompose a performance metric drop, isolate potential root causes using data dimensions, and suggest analytical steps to distinguish between technical issues and marketplace dynamics.
Analytics & ExperimentationTechnical ScreenProduct AnalyticsFunnel AnalysisRoot Cause Analysis - DoorDashMediumSoftware EngineerMake a Courier Pay Workflow Resilient to Downstream Failures
Tests your ability to implement an idempotent, fault-tolerant workflow engine that safely executes financial transactions against an unreliable downstream ledger.
Software Engineering FundamentalsTechnical ScreenDistributed SystemsIdempotencyAPI Design - DoorDashMediumMachine Learning EngineerMinimize batches and allocate riders by time
This problem tests your ability to optimize resource scheduling by grouping tasks with overlapping time windows into the minimum number of batches, subject to capacity constraints.
Coding & AlgorithmsTechnical ScreenGreedyIntervalsBin Packing - DoorDashMediumSoftware EngineerPaying Dashers Reliably When the Payment Service Is Unavailable
A system design follow-up about paying delivery drivers when the downstream payment service is unavailable. It tests reasoning about timeouts with unknown outcomes, durable records of amounts owed, retries that must never double-pay, recovery after long outages, reconciliation, and what drivers and operators see while payouts are delayed.
System DesignTechnical Screen - DoorDashMediumSoftware EngineerPooled Rideshare Earnings With Concurrent Rides and Surge
Compute a pooled-ride driver's total earnings where each minute's pay scales with how many riders are currently in the car, and minutes inside non-overlapping surge windows pay double, testing sweep-line interval counting with exact half-open boundaries and 64-bit totals.
Coding & AlgorithmsTechnical Screensweep-lineintervalsarrays - DoorDashMediumSoftware EngineerProtect SLA and Choose Storage
This question evaluates system design competencies in service reliability and storage architecture, focusing on SLA protection during upstream latency spikes and trade-offs in choosing in-memory caches (e.g., Redis), relational databases, or alternative storage for order and payment state.
System DesignTechnical Screen - DoorDashMediumSoftware EngineerRecover a Project That Has Gone Off Track
This question assesses a candidate's ability to demonstrate leadership, analytical rigor, and project management skills when facing technical or execution failures.
Behavioral & LeadershipTechnical ScreenLeadershipProject ManagementCommunication - DoorDashEasyData ScientistReduce Cold-Food Incidents with Metrics and an Insulated-Bag Experiment
Design an A/B test and metric framework to evaluate whether equipping couriers with insulated bags reduces cold-food complaints while guarding marketplace efficiency.
Analytics & ExperimentationTechnical ScreenExperimentationA/B TestingMetrics Design - DoorDashHardSoftware EngineerScale the cache to a distributed system
This question evaluates a candidate's ability to design and scale a distributed caching system, testing competencies in sharding and consistent hashing, replication and consistency models, request routing, failure detection, cache invalidation, capacity planning, and operational trade-offs for high-throughput, low-latency workloads.
System DesignTechnical Screen - DoorDashMediumData ScientistShould DoorDash add bicycle dashers?
This problem tests the ability to design an A/B test for a marketplace intervention, identify success metrics, and account for potential cannibalization and external factors in urban delivery.
Analytics & ExperimentationTechnical ScreenA/B TestingMarketplace AnalyticsCausal Inference - DoorDashMediumSoftware EngineerSimulate Constant-Time Random Courier Selection
Design a courier pool data structure that supports adding, removing, and uniformly selecting random courier IDs in average O(1) time.
Coding & AlgorithmsTechnical ScreenData StructuresHash TableArray - DoorDashMediumSoftware EngineerSolve BFS grid delivery routing
This question evaluates proficiency in grid-based graph traversal and shortest-path computation, along with complexity analysis and handling dynamic changes, assessing skills in algorithm design, graph algorithms, and data structures within the Coding & Algorithms domain.
Coding & AlgorithmsTechnical Screen - DoorDashMediumData ScientistSolve multi-part SQL with sliding windows
This question evaluates a candidate's practical SQL data-manipulation skills, including time-based rolling aggregates, distinct counts and deduplication, multi-table joins, revenue aggregation and ranking, and session/streak computations within the Data Manipulation (SQL/Python) domain.
Data Manipulation (SQL/Python)Technical Screen - DoorDashMediumSoftware EngineerSolve string match and DashMart BFS
This question evaluates proficiency with string algorithms and pattern matching, assessing understanding of substring search complexity and algorithm selection within the algorithms and data structures domain, and is commonly asked to verify efficient low-level text-search implementation and time/space trade-off reasoning at a practical implementation level. The "Dash Mart" grid BFS evaluates graph traversal, state-space modeling, and shortest-path reasoning for multi-target collection tasks within graph algorithms and path-finding, and is commonly asked to measure competence in BFS-based search, state encoding and optimization strategies with a focus on practical algorithmic application and performance considerations.
Coding & AlgorithmsTechnical Screen - DoorDashMediumSoftware EngineerValidate a Shopping Cart
Implement a shopping cart validation function that checks item availability, consolidates duplicate entries, verifies inventory levels, and ensures order minimums are satisfied.
Coding & AlgorithmsTechnical ScreenHash TableData ValidationBusiness Logic - DoorDashHardAnalytics EngineerWalk Through an ETL Project
This question evaluates an analytics engineer's end-to-end ETL and data engineering competencies, including data ingestion, transformations, data modeling, orchestration, testing, monitoring, backfills, and handling failures or schema changes.
Behavioral & LeadershipTechnical Screen - DoorDashEasyMachine Learning EngineerWalk Through an ML Project
This question assesses the candidate's ability to communicate a full-lifecycle machine learning project, focusing on problem scoping, technical execution, and business impact.
Behavioral & LeadershipTechnical ScreenML LifecycleProduct ThinkingModel Deployment - DoorDashMediumData ScientistWrite SQL for deliveries analytics
This question evaluates SQL data manipulation and analytics skills—specifically aggregation, conditional counting, time-window filtering, joins, and handling of nulls and zero-count periods.
Data Manipulation (SQL/Python)Technical Screen - DoorDashMediumData ScientistWrite SQL for percent and window changes
This question evaluates proficiency with advanced SQL techniques—specifically the use of CTEs and multiple window functions, row-level versus aggregated conditionals, time-windowed aggregations, generation of unit-day series including zero-order days, and correct handling of missing exposure labels for percent and change calculations.
Data Manipulation (SQL/Python)Technical Screen - DoorDashMediumData ScientistWrite SQL to backtest refund policy
This question evaluates advanced SQL data-manipulation and analytics competency, including time-based lateness calculations, window functions (PERCENT_RANK and rolling/partitioned windows), CTEs and joins, aggregation, and monetary-field handling for a policy-simulation scenario.
Data Manipulation (SQL/Python)Technical Screen - DropboxMediumData ScientistAnswer rapid-fire leadership checklist
This question evaluates leadership competencies in privacy decision-making, risk management, stakeholder negotiation, incident prioritization, mentoring, and end-to-end ownership within a data science and operational engineering context.
Behavioral & LeadershipTechnical Screen - DropboxMediumData ScientistCompute max island with constrained flips
This question evaluates understanding of grid-based graph connectivity, connected-component labeling, and algorithmic complexity analysis (time and space) within the Coding & Algorithms domain, emphasizing practical algorithm design for large-scale inputs.
Coding & AlgorithmsTechnical Screen - DropboxMediumData ScientistDesign enterprise file recommendations under ACLs
This problem tests the ability to design a multi-tenant recommendation system that enforces complex security constraints while maintaining low latency.
Machine LearningTechnical ScreenRecommendation SystemsMulti-tenancySecurity - DropboxMediumData ScientistExplain a project with measurable impact
This question evaluates a data scientist's ability to demonstrate end-to-end project ownership, measurable impact, causal measurement and metric validity, technical design decisions, risk mitigation, scalability reasoning, and management of security/privacy constraints.
Behavioral & LeadershipTechnical Screen - DropboxMediumData ScientistRun org-safe online experiment for recommender
This question evaluates a data scientist's competency in online experimentation design, causal inference, clustered randomization, metric definition (primary, secondary, guardrails), sample size/MDE computation, variance reduction and sequential monitoring, instrumentation and privacy-aware logging.
Analytics & ExperimentationTechnical Screen - DropboxMediumSoftware EngineerScale a file-crawling API using async jobs
This question tests the ability to design an asynchronous distributed system for crawling large file structures, focusing on job orchestration, state management, and scalability under high-latency operations.
System DesignTechnical ScreenDistributed SystemsAsynchronous ProcessingScalability - DuolingoHardSoftware EngineerDesign Animal Chess game classes
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 Animal Chess game classes states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
System DesignTechnical Screen
DoorDash
Dropbox