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Airbnb Software Engineer Interview Guide 2026

Complete Airbnb Software Engineer interview guide. Learn about the interview process, question types, and preparation tips. Practice 79+ real interview quest...

Topics: Airbnb, Software Engineer, interview guide, interview preparation, Airbnb interview

Author: PracHub

Published: 3/17/2026

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Airbnb Software Engineer Interview Guide 2026

Complete Airbnb Software Engineer interview guide. Learn about the interview process, question types, and preparation tips. Practice 79+ real interview quest...

5 min readUpdated Jun 15, 202687+ practice questions
87+
Practice Questions
4
Rounds
8
Categories
5 min
Read
Contents
TL;DRSample QuestionsAbout the Interview ProcessWhat to expectInterview roundsRecruiter screenTechnical phone screen or online assessmentOnsite coding roundsSystem design roundBehavioral / values roundAdditional manager or cross-functional round (when included)Hiring committee and team matchingWhat they testHow to stand outFAQ
Practice Questions
87+ Airbnb questions
Airbnb Software Engineer Interview Guide 2026

TL;DR

Airbnb's Software Engineer interview in 2026 typically runs as a recruiter screen, one technical screen or online assessment, and then a virtual onsite loop of several interviews. It stays grounded in classic coding and system design, but it tends to feel more discussion-heavy than a pure puzzle gauntlet. Interviewers care about more than whether you reach the right answer. Expect them to weigh how you clarify requirements, write maintainable code, reason about tradeoffs, and connect engineering decisions to user experience.

Interview Rounds
HR ScreenOnsiteTake-home ProjectTechnical Screen
Key Topics
Coding & AlgorithmsBehavioral & LeadershipSystem DesignSoftware Engineering FundamentalsML System Design
Practice Bank

87+ questions

Estimated Timeline

2–4 weeks

Browse all Airbnb questions

Sample Questions

87+ in practice bank
System Design
1.

Design a real-time chat system with hot groups

HardSystem Design

Design a real-time chat system (similar to an in-app messaging feature) that supports:

  • 1:1 and group conversations
  • Real-time delivery over WebSockets (or equivalent)
  • Message persistence and history sync
  • Read receipts (at least per-user “last read”)
  • Multi-device users (same user logged in on multiple clients)
  • High availability / disaster recovery considerations

Additional requirement:

  • The system must optimize for the Top N “hottest” group chats (e.g., groups with extremely high message throughput and/or many concurrently online participants). Explain what “hot” means and how you detect it.

The interviewer expects particular attention to:

  • A clear high-level architecture
  • A concrete data schema (tables/collections, keys, indexes)
  • How messages get routed when you have multiple WebSocket gateway servers
  • Scalability and performance trade-offs
Solution
2.

Design a booking system

MediumSystem Design

Design an online booking system (e.g., for hotel rooms, meeting rooms, or appointment slots).

Requirements

Cover the following in your design:

  • Core flows: search availability, create booking (reserve), confirm, cancel, modify.
  • Correctness: prevent double-booking under high concurrency.
  • Scale: support many users and a large inventory (properties/rooms/slots).
  • Latency: fast availability queries.
  • Reliability: handle retries, partial failures, idempotency.

Clarifications to address

  • What consistency guarantees are needed for availability and booking creation?
  • Do you need a “hold” (temporary reservation) with expiration?
  • How do you model inventory (per day, per time slot, per resource)?

Provide APIs, data model, and a high-level architecture, and discuss tradeoffs.

Solution
Coding & Algorithms
3.

Find valid split-stay listing combinations

HardCoding & AlgorithmsCoding

You are building a feature that suggests a split stay: a guest stays in one home for the first part of a trip, then switches to a second home for the remainder.

You are given:

  • A map availability from listing name (e.g., "A", "B") to a list of available day numbers (integers).
  • A requested date range [startDay, endDay] inclusive.

Return all valid split-stay options:

  1. Single-listing stay: any listing that is available for every day in [startDay, endDay].
  2. Two-listing split stay: any ordered pair (L1, L2) for which there exists a split day k with startDay <= k < endDay such that:
    • L1 is available for every day in [startDay, k], and
    • L2 is available for every day in [k+1, endDay].

Notes / clarifications:

  • Availability is per-day; treat it as a set (duplicates don’t matter).
  • A listing may appear in at most one position within a split option (i.e., L1 != L2).
  • Output can be in any order; avoid duplicates.

Example:

  • A = [1,2,3,6,7,10,11]
  • B = [3,4,5,6,8,9,10,13]
  • C = [7,8,9,10,11]
  • Query range: [3, 11]

Determine which single listings and/or two-listing split stays satisfy the rules above.

Solution
4.

Allocate refund across payments

MediumCoding & AlgorithmsCoding
Question

Given a list of completed payment transactions (each with payment method, date, and amount) and a refund amount R,

write an algorithm that issues refunds according to the rules:

Always refund in full from one payment before moving to the next.

Prefer payment methods in the priority order CREDIT → CREDIT_CARD → PAYPAL.

Within the same method, refund the most recent payment first.

Return the list of refund allocations (payment id, method, amount). Explain the algorithm’s time complexity and data structures.

Solution
Software Engineering Fundamentals
5.

Design a banking ledger for deposits/withdrawals

MediumSoftware Engineering Fundamentals

Banking/OO Design: Account Ledger

Design a minimal in-memory or persistent component for a bank account system that supports:

  • deposit(account_id, amount)
  • withdraw(account_id, amount) (reject if insufficient funds)
  • transactionHistory(account_id) (list transactions in chronological order)
  • balance(account_id)

Requirements

  • Amounts are non-negative and use correct currency precision.
  • Concurrency: multiple operations may happen at the same time for the same account.
  • Avoid double-processing if a client retries a request (idempotency).

Describe your data model and how you ensure correctness.

Solution
6.

How do you conduct a code review exercise?

HardSoftware Engineering Fundamentals

In a live interview, you are shown a short code change (a diff or a function) and asked to write code review comments with minimal back-and-forth.

What is your structured approach to producing high-signal review feedback?

Include:

  • The categories of issues you look for (correctness, edge cases, security, performance, readability, testing, API design, observability)
  • How you prioritize comments (blockers vs nits)
  • Examples of well-written review comments (what you would actually type)
  • How you handle missing context/requirements during the review
Solution
Behavioral & Leadership
7.

Explain why you want to join Airbnb

MediumBehavioral & Leadership

Behavioral Prompt: "Why Airbnb?" (Software Engineer)

Context

In a technical screen, you may be asked to concisely explain why you want to join Airbnb. The interviewer is looking for a clear, authentic narrative that connects your motivations to Airbnb’s mission and values, shows understanding of the product and ecosystem (guests and hosts), and demonstrates how your past work prepares you to make an impact in your first 6–12 months.

Task

Craft a 2–3 minute story that:

  1. Connects your motivations to Airbnb’s mission and values (e.g., belonging, hospitality, trust, thoughtful design, simplicity).
  2. Describes specific guest or host experiences that shaped your interest.
  3. Maps your past work to Airbnb’s engineering challenges.
  4. Outlines the impact you aim to make in your first 6–12 months.
Solution
8.

Walk through a project in detail

MediumBehavioral & Leadership

Walk Through a Significant Project

Provide a deep-dive on one impactful project you led or contributed to. Cover:

  1. Timeline: Start/end dates and major milestones.
  2. Scope and Objectives: What problem you solved and success criteria.
  3. Your Responsibilities: What you personally owned vs. supported.
  4. Team Composition: Who was on the team and who did what.
  5. Key Technical Decisions: Alternatives considered, trade-offs, and why you chose your path.
  6. Risks and Mitigations: What could go wrong and how you de-risked.
  7. Measurable Outcomes: Metrics (e.g., latency, availability, cost), business impact (e.g., conversion, revenue).
  8. Post-Launch Learnings: What you learned, what you’d change, next steps.
  9. Be ready to deep-dive: Design docs, PRs, experiments, data, and rollbacks.

Assume the interviewer will probe on edge cases, trade-offs, and how you validated impact.

Solution
ML System Design
9.

Design a dynamic rental pricing system

HardML System Design

System Design: ML-Driven Nightly Pricing for Short-Term Rentals

Context

Design a production ML system that recommends (and optionally auto-sets) nightly prices for hosts on a two-sided rentals marketplace. The system should maximize long-term marketplace health while protecting hosts and guests with business guardrails.

Requirements

  1. Problem formulation
    • Objective(s)
    • Constraints and business guardrails
  2. Data sources
    • Historical bookings, search demand, competitor prices, calendars, local events
  3. Feature engineering
    • Seasonality, lead time, availability/inventory, price elasticity, cancellations
  4. Modeling approach
    • E.g., time-series + gradient boosting with elasticity estimation, or constrained reinforcement learning
    • How to incorporate uncertainty and guardrails
  5. Training pipeline and evaluation
    • Offline training, offline simulation/sandboxing
  6. Online inference and architecture
    • Service design, latency and scale targets
  7. Exploration–exploitation strategy
  8. Handling cold-start listings and sparse regions
  9. Fairness, explainability, and abuse prevention
  10. Rollout plan with A/B testing and guardrail metrics
Solution
10.

Design a customer LTV prediction system

HardML System Design

System Design: End-to-End ML for Customer Lifetime Value (LTV)

Context

You are designing an end-to-end machine learning system to estimate customer lifetime value (LTV) for a large two-sided marketplace platform. Assume we are focusing on the demand side (guest/customer LTV) unless you prefer to discuss both sides; state your scope explicitly.

Requirements

Define and design the full stack from business definition and labels through modeling, evaluation, and serving. Cover the following:

  1. Business Definition
  • Precisely define LTV for this business (e.g., revenue, gross margin, contribution after variable costs). Specify which costs are included/excluded.
  • Specify the prediction horizon (e.g., 6, 12, or 24 months) and whether to discount future cash flows. State the discount rate if used.
  • Clarify scope (e.g., guest LTV only) and any exclusions (e.g., fraudulent activity, chargebacks).
  1. Data and Features
  • Enumerate data sources: bookings/transactions, cancellations/refunds, payments/fees, marketing touchpoints, user profiles/consents, search/browse events, messaging/funnel, support interactions, risk decisions, incentives, and cost tables.
  • Describe feature pipelines: aggregation windows (e.g., 7/30/90/365 days), RFM-style features, recency of activity, seasonality, geo/device, marketing channel, quality signals, and marketplace context (e.g., supply-demand).
  • Point-in-time correctness and leakage prevention (e.g., event-time joins, freeze windows). Identity resolution and PII handling.
  1. Cold-Start Strategy
  • How to score new or nearly-new users (no bookings or very sparse history). Consider priors, hierarchical grouping, and context-based features.
  1. Label Construction
  • Define the target formula precisely, including how to handle cancellations, refunds, incentives, and payment processing costs.
  • Discuss horizon alignment, censoring (users without full observation windows), and maturity/freeze windows for late-arriving data.
  1. Modeling Approach
  • Propose and justify a modeling strategy (e.g., survival/retention modeling, purchase frequency and monetary value decomposition, count models, direct regression, or mixture).
  • Note uncertainty estimation and calibration if applicable.
  1. Training/Validation
  • Specify temporal train/validation/test splits (rolling windows/backtesting). Address class/label imbalance and non-stationarity.
  1. Evaluation Metrics
  • Include regression error (e.g., MAE/RMSE/sMAPE), ranking/segment metrics (e.g., decile lift, top-k capture), calibration, and business metrics (profit at policy).
  1. Serving Architecture
  • Propose offline/online architecture for batch scoring and near-real-time updates.
  • Cover data freshness SLAs, snapshotting/backfills, point-in-time correctness, and monitoring/alerting (data quality, drift, performance, business KPIs).
  • If time is limited, you may skip detailed online serving.
  1. Downstream Use Cases and Experimentation
  • Explain how scores feed decisions (e.g., marketing budget/CPA bidding, incentives, recommendations/ranking, CRM).
  • Outline experimentation to measure impact, including interference/marketplace considerations.
  1. Risk, Bias, Privacy, and Compliance
  • Discuss how you would address model bias/fairness, privacy (consent, minimization, deletion), and regulatory requirements (e.g., GDPR/CCPA).
Solution
Other / Miscellaneous
11.

Implement retry wrapper and interdependent validators

HardOther / Miscellaneous

Context

You are implementing reusable utilities that are commonly needed in modern web apps:

  1. a robust retry wrapper for functions that can fail synchronously or asynchronously, and
  2. a small validation framework for interdependent form inputs.

The environment is JavaScript/TypeScript. You may assume access to standard Web APIs (e.g., AbortController), and you should design APIs that are ergonomic for both browser and Node runtimes.

Part A — Retry higher-order function

Implement a higher-order utility wrapWithRetry(fn, options) that wraps a JavaScript/TypeScript function which may throw synchronously or return a rejected Promise.

Requirements:

  • Retry policies:
    1. Retry once.
    2. Retry N times.
    3. Exponential backoff with configurable base/initial delay, factor, cap, and optional jitter.
  • Control which failures are retryable:
    • Allow specifying retryable error types and/or predicate functions (e.g., network errors, HTTP 5xx).
  • Timeouts:
    • Per-attempt timeout.
    • Overall timeout across all attempts.
  • Cancellation:
    • Support cancellation with AbortController/AbortSignal so the entire retrying operation can be aborted.
    • If the underlying function supports cancellation via AbortSignal, allow passing a per-attempt signal into the function.
  • Behavior and ergonomics:
    • The wrapper must handle both synchronous throws and asynchronous rejections uniformly.
    • Provide hooks to observe retries (e.g., onRetry).
    • Return or throw the final error with meaningful context.
  • Deliverables:
    • Implementation in JS/TS.
    • Usage examples.
    • Discussion of edge cases: synchronous throw vs. asynchronous rejection, non-idempotent operations, retries outliving the caller, and error propagation.

Part B — Interdependent input validators

Build a small form component with three text inputs and a tiny validation framework.

Requirements:

  1. A per-field validator that checks value length against configurable min/max.
  2. A cross-field validator that errors when any two inputs have identical values.
  3. When any input changes, revalidate other inputs as needed to keep cross-field constraints correct.
  4. Describe and demonstrate:
    • State management for values, errors, and touched/dirty state.
    • Composing per-field and cross-field validators.
    • Event triggers (onChange, onBlur, onSubmit).
    • Error display and accessibility (e.g., aria-invalid, aria-describedby).
    • Performance considerations (e.g., avoiding unnecessary revalidations, debouncing costly checks).

Provide a working example (TypeScript + React preferred) and explain your design choices and trade-offs.

Solution
12.

Implement a reservation widget with pricing

MediumOther / Miscellaneous

Implement a Lodging Reservation Widget

Context

You are building a reservation widget for a property details page. The widget must let guests select dates and guest counts, show a price, validate inputs and availability, and submit a reservation. Assume there is an API that exposes availability by date, rate rules, capacity, and a quote endpoint.

Requirements

  1. Inputs and Pricing

    • Inputs: Check-in date, Check-out date, Adults, Children.
    • Compute total price based on: nightly rate, number of nights, and dynamic pricing that depends on selected adults and children.
    • Show an itemized price breakdown.

  2. Validation

    • Check-out must be after check-in.
    • Guest counts must not exceed capacity and must satisfy rate plan rules (e.g., max adults/children, min nights).

  3. Booking Action

    • Disable the "Book Now" button if the room is sold out for any selected night or the selection is otherwise invalid.

  4. Confirmation

    • On submit, display a confirmation dialog summarizing dates, guests, and the price breakdown before finalizing.

  5. Design Artifacts to Provide

    • Describe component state, data models, and error states.
    • Outline how you would test edge cases (minimum nights, partial availability, timezone/daylight-saving transitions, and price rounding).
Solution
Product / Decision Making
13.

Prioritize consumer product opportunities

MediumProduct / Decision Making

Case: Improve Engagement in a Consumer App with Flat MAU

Context

You are given a large consumer mobile app whose monthly active users (MAU) have been flat for several months. You need to identify, evaluate, and prioritize opportunities to improve user engagement in the next quarter.

Tasks

  1. Define the primary target segment you will focus on (and why).
  2. Identify key pain points using both data and user research signals.
  3. Propose a North Star metric for engagement and a small set of supporting/diagnostic metrics (with clear definitions).
  4. Recommend 3–5 candidate solutions aimed at improving engagement. For each: state the hypothesis, expected impact, and how you would measure success.
  5. Select and apply a prioritization framework (e.g., RICE or Impact vs. Effort) to rank the solutions.
  6. Create a 90-day roadmap with milestones, success criteria, and major risks/mitigations.
Solution
Data Manipulation (SQL/Python)
14.

Review a geospatial Python module

MediumData Manipulation (SQL/Python)

You receive a Python module that processes geospatial datasets (CSV/GeoJSON) to compute distances, cluster nearby points, and write summaries. Perform a code review: identify correctness bugs, numerical issues, and edge cases (CRS mismatches, missing/invalid coordinates). Propose performance improvements (vectorization, spatial indexing such as R-tree, batching I/O), refactorings (modularization, type hints, docstrings), and security considerations (input validation, dependency pinning). Outline unit/integration tests with fixture data, estimate time/space complexity of critical paths, and suggest library choices (e.g., pandas, shapely, pyproj) with trade-offs.

Solution

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About the Interview Process

What to expect

Airbnb's Software Engineer interview in 2026 typically runs as a recruiter screen, one technical screen or online assessment, and then a virtual onsite loop of several interviews. It stays grounded in classic coding and system design, but it tends to feel more discussion-heavy than a pure puzzle gauntlet.

Interviewers care about more than whether you reach the right answer. Expect them to weigh how you clarify requirements, write maintainable code, reason about tradeoffs, and connect engineering decisions to user experience.

The onsite usually includes:

  • Two coding rounds
  • One system design round
  • One behavioral / values round

Some loops add an extra behavioral, hiring manager, or code-review-style conversation depending on the team and level. End-to-end timelines are commonly in the 2-6 week range, with senior roles sometimes taking longer because of leveling or team matching.

Interview rounds

The exact structure varies by team and level, but a typical loop looks like the following.

Recruiter screen

A short (roughly 20-30 minute) phone or video call. Expect a resume walkthrough, "why Airbnb," a discussion of your target team or product area, and practical logistics like location, compensation, and work authorization. The recruiter is assessing role fit, communication, motivation, and whether your background lines up with the kind of engineering work Airbnb is hiring for.

Technical phone screen or online assessment

Usually either a live coding interview (about 45-60 minutes) or an online assessment (around 90-120 minutes with a few coding questions). The focus is data structures and algorithms, correctness, and complexity — and how clearly you explain your thinking as you go. Airbnb tends to expect complete, near-compilable solutions, so handle edge cases and finish the implementation rather than stopping at pseudocode.

Onsite coding rounds

You'll typically face two coding rounds, each around 45-60 minutes in a shared editor. Interviewers look for readable, maintainable, working code produced collaboratively, with attention to tests and edge cases.

  • Round 1 often centers on standard algorithm patterns with some product flavor — traversal, search, pathfinding, top-k logic, iterators, or stateful behavior.
  • Round 2 may emphasize a different skill: debugging, refining an initial approach, or reasoning about tradeoffs and abstractions under pressure. Full-stack or frontend-leaning teams sometimes use more practical prompts, such as autocomplete behavior, async edge cases, or component and data-model reasoning.

System design round

A roughly 45-60 minute architecture discussion, often with a senior engineer or manager. You'll likely design a user-facing or platform system — think booking, messaging, search, recommendations, listings, or pricing — and walk through API choices, data models, scaling, reliability, and observability. Airbnb rewards product-aware design judgment over distributed-systems vocabulary, so show how your technical choices affect latency, consistency, resilience, and the user experience.

Behavioral / values round

A conversational interview, usually about 45 minutes. Expect questions on ownership, collaboration, conflict, ambiguity, failure, resilience, and why Airbnb's mission resonates with you. Interviewers map your past behavior to Airbnb's values, with particular attention to mission alignment, empathy, adaptability, and resourcefulness.

Additional manager or cross-functional round (when included)

When present, this is often a 30-45 minute conversation with a manager or cross-functional interviewer. It tends to dig into team fit, stakeholder management, product thinking, and your level of impact. For experienced candidates, it can also inform leveling by probing breadth of ownership, influence, mentoring, and decision-making with incomplete information.

Hiring committee and team matching

This stage is usually internal rather than a formal interview, though experienced candidates may have follow-up team conversations. Airbnb uses it to calibrate level, review consistency across feedback, and align you with a team. Any live conversations here are typically about background, fit, and team context rather than a fresh coding round.

What they test

Coding fundamentals. Be comfortable with arrays, strings, hash maps, sets, trees, graphs, heaps, recursion, backtracking, dynamic programming, sorting, searching, BFS, DFS, shortest path, and custom-iterator or data-structure problems. Solving the problem is only part of the bar — you're also expected to write clean code, name things well, handle edge cases, state time and space complexity, and discuss how you'd test your solution.

Product-aware system design. Be ready to reason through scalable backend architecture, APIs, data modeling, caching, queues, async workflows, reliability, failure handling, and monitoring — and the tradeoffs between latency, consistency, and simplicity. The strongest answers connect design choices to real Airbnb-style flows like booking, messaging, search, recommendations, trust and safety, and listings. Depending on the team, you may also be evaluated on practical instincts such as code-review judgment, rollout strategy, experimentation, debugging, and collaboration with product and design.

Scope for senior and staff roles. At higher levels, the evaluation expands beyond individual execution to architecture leadership, operational quality, incident handling, mentoring, cross-team influence, and how you make long-term technical decisions under ambiguity.

How to stand out

  • Write production-quality code, not just a correct algorithm. Use clear naming, sensible decomposition, and explicit edge-case handling from the start.
  • Clarify before you code, especially when a prompt sounds product-flavored. Airbnb interviewers tend to reward collaborative problem solving over silent brute-forcing.
  • Narrate your tradeoffs. Explain why you chose a data structure, what complexity you're targeting, and how you'd validate correctness with test cases.
  • Tie design decisions to user impact. When you discuss caching, consistency, or async processing, connect it to booking reliability, search freshness, messaging latency, or host and guest experience.
  • Prepare values-aligned behavioral stories. Map examples to mission alignment, empathy, adaptability, and resourcefulness. Stories that show ownership, inclusion, and user-centered decisions land better than generic leadership anecdotes.
  • Expect follow-ups. Interviewers often push past your first answer on scaling constraints, failure modes, maintainability, or how a design evolves over time.
  • For senior roles, lead with scope and influence. Show how you aligned stakeholders, improved reliability, mentored others, or drove architecture decisions through ambiguity — not just technical correctness.

Frequently Asked Questions

I’d call it hard but fair. It felt more selective than a lot of big tech loops because Airbnb seems to care about both coding strength and whether you make practical engineering decisions. The coding parts were not always trick questions, but they did expect clean communication, solid testing instincts, and thoughtful tradeoffs. System design mattered more than I expected, even for non-staff roles. If you’re strong at LeetCode but weak at explaining decisions, the process can feel tougher than it looks.

The process usually starts with a recruiter chat, then a technical screen that focuses on coding. After that, there’s often a full onsite or virtual onsite with several rounds. In my experience, that meant coding interviews, a system design round for mid-level and above, and a behavioral or values-focused conversation. Some teams also add a hiring manager chat or team match step. The exact mix can change by level, but expect both problem solving and discussion about how you work with others.

For most people, I’d budget four to eight weeks if you already have a decent base. If you’re rusty on algorithms or haven’t done interviews in a while, give yourself longer. What helped me most was splitting prep into coding, system design, and behavioral stories instead of grinding only problems. I’d do timed coding practice a few times a week, then spend separate sessions on design and communication. Airbnb’s process rewards people who sound like real engineers, not just fast puzzle solvers.

Coding fundamentals matter first: arrays, strings, hash maps, trees, graphs, recursion, BFS and DFS, and basic dynamic programming. Beyond that, I’d pay real attention to writing readable code, handling edge cases, and talking through tradeoffs. For mid-level and senior roles, system design is a big deal, especially APIs, data modeling, scaling, caching, and reliability. Behavioral prep also matters more than candidates think. Be ready to talk about project impact, disagreements, ownership, and how you balance speed with good engineering judgment.

The biggest mistake I saw was treating Airbnb like a pure algorithms interview and ignoring communication. People jump into coding too fast, don’t clarify requirements, and never step back to explain why their approach makes sense. Another common problem is messy code with no testing thought process. In design rounds, weak candidates stay vague and avoid tradeoffs. In behavioral rounds, generic answers hurt a lot. Airbnb seems to like people who are thoughtful, collaborative, and product-minded, not just technically fast under pressure.

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