In this mock coding interview from interviewing.io, a software engineer with 3 years of industry experience and a freshly completed master's degree preps for an upcoming onsite. The interviewer, a Meta engineer with prior experience at another major tech company, walks the candidate through two algorithm problems, covering greedy sorting and prefix set precomputation, before wrapping up with detailed feedback. 🧩 The Problems: Two City Scheduling & Longest Common Prefix **Two City Scheduling:** Given a 2D cost array where each entry represents the cost to fly a person to either City A or City B, determine the minimum total cost to fly exactly n people to each city. The candidate identifies a greedy approach — sorting by the cost differential (A minus B) and splitting the sorted array down the middle. **Longest Common Prefix:** Given two arrays of positive integers, find the length of the longest common prefix shared between any pair of integers (one from each array). The candidate precomputes all integer prefixes for the first array into a set using repeated integer division by 10, then searches each element of the second array against that set. Chapters 0:00 — Introductions and candidate background 2:00 — Problem 1 introduced: Two City Scheduling 3:28 — Greedy approach walkthrough and dry run 10:04 — Coding the solution and testing 17:46 — Problem 2 introduced: Longest Common Prefix 21:09 — Prefix set approach and complexity analysis 25:31 — Coding the solution 40:42 — Testing and dry run of solution 46:07 — Feedback and self-evaluation Concepts Greedy Algorithm Design - Sorting by cost differential (A minus B) to optimally assign people to cities - Leveraging the guaranteed even split (2n people) to simplify the greedy decision boundary - In-place sorting to avoid unnecessary extra space Prefix Precomputation with Sets - Precomputing all integer prefixes for array 1 into a hash set using integer division by 10 - Avoiding brute-force O(n²) pair comparisons by reducing lookup to O(1) per query - Early stopping in the inner loop when a matching prefix is found or the number reaches zero Complexity Analysis - Two City Scheduling: O(n log n) time due to TimSort, O(1) extra space - Longest Common Prefix: O(n·L) time and space, where L is the number of digits in the longest integer - Noting that math.log10 can compute digit length in O(1) vs. an iterative digit-counting loop Code Quality & Robustness - Wrapping logic in a proper function declaration for clean testability - Handling the zero-prefix edge case by defaulting curr_prefix_length to 0 - Performing a verbal dry run of the second solution before testing to catch logic errors proactively Interview Communication & Execution - Thinking out loud with high confidence while keeping the reasoning structured - Balancing verbosity — thorough explanation is generally a positive, but checking in with the interviewer periodically helps maintain alignment - Self-identifying areas for improvement (fixation on top-down digit extraction, rambling) during the self-evaluation 👉 Book coaching or watch more mock interviews: https://www.interviewing.io 📝 Interview transcript & feedback: https://interviewing.io/mocks/meta-python-two-city-scheduling-longest-common-prefix 🔗 Explore more Meta interviews: https://interviewing.io/mocks?company=meta Disclaimer: All interviews are shared with explicit permission from the interviewer and interviewee. All candidates remain anonymous.

In this mock system design interview, a senior backend engineer preparing for an upcoming interview walks through the design of a coding practice platform — covering everything from requirements gathering and API design to async job execution and leaderboard architecture. The interviewer, an ML engineer with over 12 years of experience at top tech companies, guides the session and provides detailed feedback at the end. 🧩 The Problem: Design a Coding Practice Platform Design a system similar to a well-known competitive programming and interview prep platform. The system must support browsing and filtering problems by difficulty and language, providing an in-browser IDE, submitting code for evaluation, and returning results asynchronously. Stretch goals include supporting large-scale competitions with up to 100,000 simultaneous participants and a real-time leaderboard. Core challenges include securely isolating code execution environments, handling async job processing at scale, and choosing appropriate storage strategies for structured and unstructured data. Chapters 0:00 Introductions and session setup 4:41 Functional requirements 7:54 Non-functional requirements and back-of-napkin estimates 13:08 API design 18:58 High-level architecture and submission pipeline 39:48 Competitions and leaderboard design (stretch goal) 44:24 Feedback and improvement areas Concepts Requirements & Scope Definition - Separating core features (problem browsing, code submission, result polling) from stretch goals (competitions, leaderboard) - Estimating DAU/MAU and deriving peak submission throughput and pod concurrency - Defining latency targets per subsystem (e.g., search vs. result delivery) API & Entity Modeling - Structuring REST endpoints around distinct entities: problems vs. submissions - Using entity modeling upfront to motivate API shape and storage choices - Returning a submission ID immediately on POST to enable async polling Async Job Execution Architecture - Using a message queue to decouple the submission service from pod orchestration - Running each submission in a secure, isolated container with CPU, memory, and filesystem constraints - Polling for job status via submission ID rather than holding open connections Queue Design & Failure Recovery - Trade-offs between Kafka and SQS for work-queue patterns (visibility locks, at-least-once delivery) - Handling pod crashes: health checks, retries, dead-letter queues - The risk of dequeuing messages before job completion and strategies to avoid data loss Storage Strategy (Polyglot Persistence) - Using a relational database for structured entities (problems, submissions, users) that require joins - Storing unstructured data — problem descriptions, test cases, images — in object storage (S3) - Using Redis sorted sets for leaderboard data, updated via Change Data Capture from the primary database Scalability & Extensibility - Horizontal scaling of the submission service and database sharding for competition-scale load - Kubernetes HPA for dynamic pod scaling with Kafka as a buffer during scale-up lag - Redis sorted sets as an efficient, low-latency leaderboard data structure 👉 Book coaching or watch more mock interviews: https://www.interviewing.io 📝 Interview transcript & feedback: https://interviewing.io/mocks/faang-system-design-coding-practice-platform 🔗 Explore more Faang interviews: https://interviewing.io/mocks?company=faang Disclaimer: All interviews are shared with explicit permission from the interviewer and interviewee. All candidates remain anonymous.

In this mock system design interview from interviewing.io, an experienced software engineer designs a full backend API for an online bookstore — starting from scratch and progressively layering in search, caching, image handling, and AI-powered recommendations. The discussion covers both foundational distributed systems concepts and cutting-edge topics like vector embeddings and agentic search with MCP servers. 🧩 The Problem: Online Bookstore Backend API A brick-and-mortar bookstore wants to launch an online presence and needs a complete backend API built from the ground up. The system must support browsing and searching a catalog of up to 10 million books, handle millions of reads per hour, manage inventory levels accurately, and eventually incorporate AI-driven semantic search and an agentic chat experience to help users discover books. Chapters 0:00 Introduction & setup 2:02 Functional and non-functional requirements 5:23 High-level architecture (services, load balancer, database) 8:09 Search design: Elasticsearch vs. Postgres and fuzzy matching 12:33 Caching strategy with Redis and inventory freshness tradeoffs 20:24 API design, authentication, image uploads, and Kafka-based thumbnail pipeline 31:27 Adding AI: vector embeddings, semantic search, and agentic MCP-based chat 47:50 Interviewer feedback Concepts Requirements & Scope Definition Separated functional requirements (read, write, search) from non-functional ones (low latency, high availability, freshness) Anchored scale estimates to a real-world analog (large national bookstore chain) to justify millions of reads per hour and a catalog of 1–10 million books Identified that write traffic would be low and admin-only, shaping the overall architecture early System Architecture & Service Design Split read and write paths into independent services to allow separate scaling Placed a load balancer at the edge to handle authentication, rate limiting, and routing Introduced S3 for image storage with pre-signed URLs to offload upload traffic from application servers Used Kafka and a worker pool for asynchronous image compression and thumbnail generation at multiple sizes Search & Data Modeling Chose Elasticsearch for fuzzy and metadata-based searches, reserving Postgres for exact-match lookups (e.g., by ISBN) Designed a relational schema with a books table, a separate authors table, and foreign key joins Added Redis caching in front of Postgres for hotkey mitigation, with differentiated TTLs based on inventory status (shorter for in-stock items, longer for out-of-stock) AI Integration: Embeddings & Agentic Search Proposed a nightly batch ETL pipeline to generate vector embeddings via a third-party embedding API, stored in a vector database and surfaced through Elasticsearch's vector search support Designed a circuit breaker for the third-party embedding API outages, and a separate dead letter queue to hold messages if the internal database goes down, so paid-for embeddings aren't lost Outlined an agentic search path using an MCP server to expose tool calls (e.g., semantic search) to an LLM agent, with WebSocket streaming routed through an L4 load balancer and a Redis-backed connection registry for session routing across scaled WebSocket servers Interview Execution & Communication Strong opening with functional/non-functional requirement framing and proactive mention of auth and observability Interviewer noted the candidate drove edge cases more confidently in the AI section than in the foundational design — a reminder to bring the same proactive energy to all parts of a design Feedback highlighted the value of writing out concrete numbers (DAU, latency targets, data volume) from the start rather than arriving at them through follow-up prompts Advised always stating the rationale for technology choices (e.g., why Elasticsearch, why Kafka) rather than assuming the reasoning is implied 👉 Book coaching or watch more mock interviews: https://www.interviewing.io 📝 Interview transcript & feedback: https://interviewing.io/mocks/faang-system-design-online-bookstore 🔗 Explore more Faang interviews: https://interviewing.io/mocks?company=faang Disclaimer: All interviews are shared with explicit permission from the interviewer and interviewee. All candidates remain anonymous.

In this mock system design coaching session, a software engineer preparing for an upcoming system design interview works through a high-scale inventory and order management problem with an experienced interviewer. The session is structured as a hybrid mentoring and mock interview, with the interviewer providing real-time feedback throughout rather than saving it all for the end. 🧩 The Problem: Amazon Prime Day Order Processing System Design a system that handles over 1 million orders per minute during a peak shopping event. Each order must reserve inventory, charge the customer, and confirm the order — all while preventing overselling, avoiding duplicate charges, and maintaining real-time inventory counts visible to other buyers. The core challenge is managing high-contention inventory updates at massive global scale while keeping the user experience fast and accurate. Chapters - 0:00 Introduction and session format - 3:21 Problem statement and scale framing - 6:23 Functional requirements and scope narrowing - 15:05 Non-functional requirements and consistency vs. availability - 18:31 Data models and API design (REST) - 23:48 High-level architecture and service decomposition - 47:54 Failure modes, resilience, and message queues - 57:25 Final feedback and interview execution tips Concepts Requirements & Scope Framing - Narrowing the problem to the critical workflow: item selection → cart reservation → checkout - Identifying which adjacent concerns (catalog pricing, warehouse dispatch) can be delegated to existing services - Prioritizing consistency over availability when overselling is unacceptable Service Architecture & Decomposition - Separating order service and inventory service to reduce blast radius at scale - Using an API gateway early for auth, load balancing, and rate limiting - Modeling order lifecycle states (in-cart, in-checkout, paid, completed) Data Modeling & API Design - Inventory table tracking total stock vs. reserved quantities per product - Order table capturing user ID, product ID, quantity requested vs. fulfilled, status, and TTL via updated-at timestamps - Choosing REST for straightforward, noun-based access patterns rather than something like GraphQL — the candidate was candid that familiarity was the primary driver ("I just know REST"), and the interviewer confirmed this is a perfectly reasonable answer as long as you have one ready Scalability & Resilience - Horizontal scaling of services across availability zones and global regions to handle 1M orders/minute - Introducing message queues between services to avoid losing requests when instances go down - Dead letter queues for handling failed message processing - The interviewer offered a real-world illustration (credit card pre-authorization checks starting before a customer finishes typing their card number) to demonstrate how background/async processing can shave perceived latency — not a component the candidate had incorporated into their own design Interview Execution & Communication - Get to the diagram ("boxes") as early as possible — verbal-only discussion burns time without preserving design decisions - Make explicit decisions rather than listing options; state the trade-off, then commit to one path - Annotate data flows on the diagram (what payload is passed between each service) so the interviewer can follow along without relying on verbal narration alone 👉 Book coaching or watch more mock interviews: https://www.interviewing.io 📝 Interview transcript & feedback: https://interviewing.io/mocks/faang-system-design-inventory-orders 🔗 Explore more system design interviews: https://interviewing.io/mocks Disclaimer: All interviews are shared with explicit permission from the interviewer and interviewee. All candidates remain anonymous.

In this mock system design interview, an experienced full-stack engineer with a startup and founder background practices designing the backend of a dating application. The interviewer, a seasoned backend and infrastructure engineer with experience at large tech companies, guides the candidate through requirements, service architecture, geospatial querying, and scaling strategies, wrapping up with detailed feedback on strengths and areas to improve. 🧩 The Problem: Dating App Backend (Medium) Design the backend for a dating application where users can log in, manage a profile, view nearby users, and maintain a favorites list. The system must support 1 million daily active users, prioritize availability for location-based queries, serve fresh real-time location data, and handle geospatial proximity searches efficiently — all without a matching system. Chapters - 0:00 Introductions and goal-setting - 2:17 Problem statement and clarifying questions - 3:13 Functional and non-functional requirements - 6:28 API design (profile, location, getNearby, favorites) - 10:13 High-level architecture (profile service vs. location service) - 13:00 Database schema, geospatial indexing, and query design - 30:19 Scaling, caching, denormalization, and profile pictures - 42:09 Reliability, analytics, and feedback Concepts Requirements & Product Scope - Distinguishing strong consistency needs (profile management) from availability-first needs (nearby queries) - Setting latency targets and freshness expectations for a mobile, event-context use case - Identifying the getNearby endpoint as the heaviest and most interesting query to optimize Service Architecture & API Design - Separating concerns into a profile service (Postgres) and a location service with geospatial capabilities - Designing REST endpoints for profile CRUD, periodic location updates, getNearby, and favorites - Using JWT/header-based identity to avoid redundant lookups in hot-path queries Geospatial Data Modeling & Query Design - Leveraging PostGIS (R-tree indexes) for efficient radius-based proximity queries over lat/long coordinates - Structuring a user location table with a geospatial index and a datetime index to filter for active/fresh locations - Using a cron job (backed by a datetime index) to purge stale location records, with profile-update invalidation as a faster primary path Scalability & Cost Optimization - Denormalizing profile data into the location table to eliminate joins on the hot-path getNearby query - Using Redis caching in front of the location service to absorb repeated refreshes from the same user - Geographic sharding by continental region to improve data locality for geospatial index scans - Storing pre-signed S3 URLs rather than regenerating them per request; compressing profile images to reduce network volume Reliability, Analytics & Privacy - Running read replicas and hot-standby database failover for high availability - Streaming data off production to a data lake (e.g., Databricks or S3) for analytics workloads, starting with reader-replica access and evolving toward nightly exports or real-time streaming - Handling GDPR-driven user deletion with cleanup jobs; flagging stalker/safety tooling as a product-driven requirement Interview Execution & Communication - Strong product intuition: quickly identifying which endpoint drives complexity and cost - Opportunity to go deeper on database internals — index types, compound indexes, and query planner behavior - Caching discussion benefited from specificity around Redis key structure, cache invalidation strategy, and hit-rate optimization - Whiteboard or diagram usage recommended once ASCII-style inline notation reaches its complexity limit 👉 Book coaching or watch more mock interviews: https://www.interviewing.io 📝 Interview transcript & feedback: https://interviewing.io/mocks/faang-system-design-dating-app 🔗 Explore more faang interviews: https://interviewing.io/mocks?company=faang Disclaimer: All interviews are shared with explicit permission from the interviewer and interviewee. All candidates remain anonymous.

In this mock coding interview, a senior backend engineer with 10+ years of experience prepares for upcoming final-round interviews at top tech companies. The session — conducted by a data engineer at Atlassian with over five years of interviewing experience — covers two coding problems in C++, followed by detailed feedback on pacing, communication, and interview strategy. Enjoy, and good luck with your own interviews! 🧩 The Problems: Longest Consecutive Sequence (Medium) + Tree Cliff Grid (Hard) The first problem asks the candidate to find the length of the longest consecutive integer sequence in an unsorted array, solved optimally in O(n) time using a hash set. The second is a custom grid problem: given a 2D cliff modeled as a binary grid where trees are stable only if connected to the bottom row, determine the resulting grid after cutting a specified tree and letting all newly unstable trees fall. Chapters 0:00 Candidate and interviewer backgrounds 3:36 Problem 1: Longest Consecutive Sequence — problem walkthrough 9:25 Problem 1: Hash set approach, coding, and test cases 25:07 Problem 2: Tree Cliff Grid — problem walkthrough 30:08 Problem 2: BFS approach and coding 1:01:03 Feedback: pacing, communication, and interview strategy Concepts Problem-Solving Approach - Recognizing when sorting (O(n log n)) is valid versus when a hash set enables O(n) linear time - Using a sentinel value (–1) to encode visited state and final output in a single pass - Starting BFS/DFS only from confirmed "anchor" nodes (bottom row) to avoid redundant traversal Hash Set & Graph Traversal - Building an unordered set for O(1) membership lookup - Identifying sequence start points by checking for the absence of a predecessor - Multi-source BFS from the bottom row to mark all stably connected trees Edge Case Handling - Empty input, single-element arrays, and negative integers for the sequence problem - Cutting a tree in the bottom row versus an interior node in the grid problem - Avoiding duplicate queue insertions by marking nodes immediately upon enqueue C++ Implementation Details - Using unordered_set, queue, and structured bindings for clean BFS code - Correctly bounding row and column indices (NR ‹ M, NC ‹ N) to prevent out-of-bounds access - Resetting grid values in-place to avoid extra space for a visited array Interview Execution & Communication - Asking targeted clarifying questions (subarray vs. sequence, input constraints) before coding - Balancing over-explanation on easier problems to preserve time for harder follow-ups - Framing debugging out loud ("I feel like I'm one small error away") as a natural nudge for hints rather than a direct ask - Taking initiative on test cases and complexity analysis without waiting for interviewer approval 👉 Book coaching or watch more mock interviews: https://www.interviewing.io 📝 Interview transcript & feedback: https://interviewing.io/mocks/faang-cpp-longest-consecutive-sequence-tree-cliff 🔗 Explore more FAANG interviews: https://interviewing.io/mocks?company=faang Disclaimer: All interviews are shared with explicit permission from the interviewer and interviewee. All candidates remain anonymous.

In this mock programming interview, a software engineer with 3.5 years of experience preps for an upcoming interview by tackling a hard algorithmic problem. Watch as the interviewee works through problem clarification, proposes a brute-force baseline, and then derives an efficient recursive quad-search solution...all while managing time and communicating their thought process clearly. 🧩 The Problem: Counting Black Holes with Quadtree Binary Search (Hard) Given the bottom-left and top-right coordinates of a 2D region of space (with coordinate values ranging from 0 to 1,000), and a black-box helper function blackhole_present(bottom_left, top_right) that returns true if one or more black holes exist within a given sub-region, design an efficient algorithm to count the total number of black holes in the region. The challenge is that each call to the helper function is expensive, so a brute-force, cell-by-cell scan is impractical; the solution must minimize the number of calls using a divide-and-conquer, quad-search strategy. Chapters - 0:00 Introductions & interview format - 4:10 Problem statement presented - 7:31 Clarifying questions: inputs, outputs, and constraints - 13:56 Brute-force approach & transition to binary/quad search - 30:41 Coding the recursive quad-search solution - 44:50 Dry run & edge case walkthrough - 50:40 Feedback session Concepts Problem Clarification & Constraint Gathering - Confirmed inputs (bottom-left and top-right coordinate pairs) and output (integer count of black holes) - Identified coordinate range (0–1,000 for both x and y axes) - Clarified behavior of the helper function, including single-cell queries and boundary conditions Brute-Force Baseline & Optimization Motivation - Proposed iterating over every cell as an O(n) time, O(1) space baseline - Identified the practical constraint on total helper function calls as the driver for optimization - Used the brute-force analysis to justify transitioning to a logarithmic approach Divide-and-Conquer (Quadtree) Strategy - Key insight: splitting a 2D region requires dividing into four quadrants, not two halves - Recursive structure: if a region returns false, prune immediately; if true, subdivide further - Base cases: return 0 if no black hole present, return 1 if the region collapses to a single coordinate point Coordinate Handling & Edge Cases - Carefully computed midpoints for both x and y axes to define quadrant boundaries - Added boundary guards to prevent out-of-bounds calls to the helper function (e.g., mid_x + 1 ≤ top_x) - Discussed edge cases: empty grid, single-row or single-column regions, and single-point regions Time & Space Complexity Analysis - Time complexity: O(m log n), where m is the number of black holes and n is the total number of cells - Space complexity discussion: tied to the recursive call stack depth rather than the black hole count - Interviewer noted the importance of thoroughly justifying space complexity, not just stating the result Interview Execution & Communication - Strong proactive communication: thought process was visible throughout, clarifying questions were well-timed - Brute-force solution was proposed early and used as a written baseline for optimization - Areas for improvement: read the problem output specification more carefully before proposing a return type; dry-run the code proactively rather than waiting to be prompted; consider wrapping the solution in an input-validation function for added polish 👉 Book coaching or watch more mock interviews: https://www.interviewing.io 📝 Interview transcript & feedback: https://interviewing.io/mocks/faang-python-black-hole-quadtree-search 🔗 Explore more FAANG interviews: https://interviewing.io/mocks?company=faang Disclaimer: All interviews are shared with explicit permission from the interviewer and interviewee. All candidates remain anonymous.

In this mock machine learning interview, a data science student tackles fundamental ML concepts with a faang engineer. Watch as they explore supervised vs unsupervised learning, dive deep into algorithms like K-means clustering and decision trees, discuss neural network training with backpropagation, and attempt to model a real-world product recommendation system. The interview showcases both strong theoretical foundations and areas for practical improvement. 🧩 The Problem: ML Engineering Fundamentals (Medium) This technical interview covers core machine learning concepts including supervised/unsupervised learning algorithms, model training techniques, overfitting prevention, neural network architecture, loss functions, optimization methods, and practical problem modeling. The candidate must demonstrate understanding of both theoretical concepts and their real-world applications, culminating in designing an ML solution for Amazon's product recommendation system. Chapters 0:00 - Introduction and background 2:30 - Supervised vs unsupervised learning 3:21 - K-means clustering deep dive 8:48 - Decision trees and Gini impurity 17:27 - Overfitting and underfitting concepts 21:37 - Neural network training and backpropagation 37:52 - Practical ML problem modeling 43:58 - Interview feedback and discussion Concepts Learning Algorithm Fundamentals - Supervised learning with labeled data and targets - Unsupervised pattern recognition without labels - Algorithm selection based on problem type - Distance metrics and optimization objectives Model Training and Optimization - Gradient descent and parameter updates - Loss function selection for different problem types - Learning rate scheduling and convergence strategies - Backpropagation for neural network weight updates Overfitting Prevention and Regularization - Model complexity control through hyperparameters - Training vs validation performance monitoring - Regularization techniques like L1 penalty terms - Feature engineering and dimensionality reduction Practical Problem Modeling - Translating business problems into ML frameworks - Training data construction and feature representation - Supervised learning for recommendation systems - User-item interaction modeling and probability prediction 👉 Book coaching or watch more mock interviews: https://www.interviewing.io 📝 Interview transcript & feedback: https://interviewing.io/mocks/faang-ml-engineering-fundamentals-1 🔗 Explore more faang interviews: https://interviewing.io/mocks?company=faang Disclaimer: All interviews are shared with explicit permission from the interviewer and interviewee. All candidates remain anonymous.

In this mock machine learning interview, a senior college student studying AI applications walks through their background and experience before fielding questions on core ML concepts. Drawing on their self-directed ASL-to-speech translation project as a practical example, the conversation covers supervised vs unsupervised learning, feature engineering with MediaPipe hand landmarks, overfitting and regularization techniques, and the general training process. The interviewer closes with feedback on the value of pairing theoretical grounding with hands-on experimentation. 🧩 Mock Machine Learning Interview (Beginner) A general machine learning mock interview covering core concepts including supervised learning, overfitting, and regularization, using the candidate's self-directed ASL-to-speech translation project as the running practical example throughout. Chapters - 0:00 Introduction and candidate background - 2:32 Supervised vs unsupervised learning fundamentals - 4:43 Deep dive into ASL translation project details - 15:19 Model architecture and training process discussion - 20:51 Training methodology and gradient descent concepts - 25:09 Overfitting challenges and solutions - 29:31 Regularization techniques and model complexity - 37:18 Feedback and learning recommendations Concepts Machine Learning Fundamentals - Supervised learning with labeled datasets vs unsupervised clustering approaches - Common algorithms like regression, decision trees, and neural networks - Training data format and feature engineering considerations Model Architecture & Implementation - Transitioning from CNN to MLP with coordinate-based features - Using MediaPipe for hand landmark detection and coordinate extraction - Feature engineering with 160+ attributes including distances, angles, and finger positions Training Process & Optimization - Understanding model parameter updates during training - Introduction to gradient descent and backpropagation concepts - Performance evaluation on training vs test datasets Overfitting & Regularization - Recognizing when models memorize training data vs learning generalizable patterns - Techniques like dropout, early stopping, and noise injection - Balancing model complexity with generalization ability Data Quality & Preprocessing - Challenges with synthetic vs real-world training data - Using cosine similarity to validate dataset consistency - Importance of diverse, representative training examples 👉 Book coaching or watch more mock interviews: https://www.interviewing.io 📝 Interview transcript & feedback: https://interviewing.io/mocks/ML-Behavioral-Interview-1 🔗 Explore more FAANG interviews: https://interviewing.io/mocks?company=faang Disclaimer: All interviews are shared with explicit permission from the interviewer and interviewee. All candidates remain anonymous.

In this mock systems design interview, a frontend engineer preparing for their onsite interviews tackles designing a dating application backend. Watch as an experienced FAANG engineer guides them through architecting a system that handles user profiles, location-based matching, and authentication at scale. The discussion covers database design, API endpoints, scalability considerations, and practical implementation details for handling millions of daily active users. 🧩 The Problem: Design a Dating Application (Medium) Design an architecture for a dating application where users can log in and see other nearby users. The system needs to handle user authentication, profile management, location-based queries, and scale to support 1 million daily active users. Key considerations include database schema design, API endpoint structure, and performance optimization strategies. Chapters - 0:00 Introduction and background - 2:30 Problem breakdown and requirements gathering - 6:29 System architecture and technology selection - 17:46 API server design and Express discussion - 23:46 Create profile endpoint flow - 26:25 Database schema and user model design - 44:42 Get nearby profiles endpoint design - 54:22 Advanced topics: matches, pagination, and latency - 57:28 Interview feedback and recommendations Concepts Requirements & System Scope - Identifying core features from problem keywords - Scaling considerations for 1M daily active users - Balancing feature complexity with time constraints - Prioritizing breadth over depth in initial design Database Design & Architecture - Document vs relational database tradeoffs - Schema design for user profiles and location data - Index strategy for common query patterns - Read-heavy vs write-heavy system considerations API Design & Implementation - RESTful endpoint structure and HTTP status codes - Rate limiting strategies using IP-based controls - Authentication flow and middleware placement - Pagination approaches for large result sets Scalability & Performance - Load balancer placement and multi-instance servers - CDN usage for static content delivery - Caching strategies with Redis for hot data - Database sharding considerations and thresholds Interview Communication & Strategy - Moving quickly through breadth before diving deep - Having default technology choices ready - Acknowledging but not over-engineering edge cases - Demonstrating practical backend experience 👉 Book coaching or watch more mock interviews: https://www.interviewing.io 📝 Interview transcript & feedback: https://interviewing.io/mocks/faang-system-design-dating-app-backend 🔗 Explore more faang interviews: https://interviewing.io/mocks?company=faang Disclaimer: All interviews are shared with explicit permission from the interviewer and interviewee. All candidates remain anonymous.

In this mock behavioral interview, a software engineer with 6 years of experience practices answering common behavioral questions with an experienced interviewer. Watch as they work through questions about technical decision-making and conflict resolution, with detailed feedback on the STAR methodology, story selection, and how to showcase technical expertise in behavioral responses. 🧩 Behavioral Interview Preparation (Medium) Practice answering behavioral interview questions effectively, focusing on technical decision-making and conflict resolution scenarios. The challenge involves structuring responses using the STAR methodology, selecting appropriate stories that demonstrate technical leadership, and communicating soft skills while highlighting engineering expertise. Chapters - 0:00 Introduction and format discussion - 1:19 Tell me about yourself practice - 5:49 Technical decision question - 8:25 Feedback on technical storytelling - 20:37 STAR methodology deep dive - 24:54 Conflict resolution question - 30:57 Conflict resolution feedback - 42:23 Ideal team player characteristics - 46:57 Book recommendation and final advice Concepts: Story Selection & Technical Focus - Choose stories that highlight engineering decisions, not just product choices - Include specific technologies, frameworks, and technical trade-offs - Ensure the protagonist role is clear and technically substantive - Balance product context with technical implementation details STAR Methodology Structure - Situation: Set context with when, where, and team composition (30-60 seconds) - Task: Reiterate the problem statement in first person (10-15 seconds) - Action: Detailed technical approach and implementation (4-5 minutes) - Result: Summary, lessons learned, and impact measurement (30-60 seconds) Conflict Resolution Communication - Use specific buzzwords like "cross-functional collaboration" and "open communication" - Focus on individual relationships rather than team-vs-team dynamics - Demonstrate emotional intelligence and compromise strategies - Show technical problem-solving alongside interpersonal skills Answer Precision & Keywords - Use exact words from the interviewer's question in your response - Pay attention to strong words like "conflict" and "resolve" - Reference established frameworks and books when appropriate - Clarify ambiguous questions before answering Interview Execution Best Practices - Structure answers with intentional pauses between STAR components - Include technical terminology naturally throughout responses - Demonstrate passion for specific engineering principles - Close with clear summaries using numbered takeaways 👉 Book coaching or watch more mock interviews: https://www.interviewing.io 📝 Interview transcript & feedback: https://interviewing.io/mocks/fanng-behavioral-interview-3 🔗 Explore more faang interviews: https://interviewing.io/mocks?company=faang Disclaimer: All interviews are shared with explicit permission from the interviewer and interviewee. All candidates remain anonymous.

In this engineering manager behavioral mock interview, an experienced candidate with a startup founder background practices for their upcoming interviews at larger tech companies. The interviewer provides detailed feedback on the STAR methodology, storytelling techniques, and how to effectively demonstrate leadership skills through past experiences. 🧩 Engineering Manager Behavioral Interview (Medium) Navigate common behavioral interview questions for engineering management roles, focusing on decision-making, team dynamics, and leadership challenges. The candidate must demonstrate their ability to handle complex situations, make tough decisions, and lead teams effectively while showing growth, humility, and technical understanding. Chapters - 0:00 Introduction and candidate background - 3:35 First behavioral question: changing project course - 9:10 Detailed STAR methodology feedback and analysis - 21:58 Advanced storytelling techniques and leadership theories - 48:04 Second question: managing good performer, poor team player - 58:55 Final feedback and improvement areas Concepts STAR Methodology Structure - Situation setup in 30 seconds with proper context - Task reiteration using keywords from the original question - Action as the main body (3-4 minutes) with strategic pauses - Result summary with lessons learned and growth demonstration Leadership Storytelling Techniques - Making situations appropriately challenging to demonstrate value - Introducing multiple characters to show team awareness - Balancing confidence with humility through failure stories - Using specific metrics and outcomes to validate success Advanced Interview Differentiation - Referencing leadership frameworks and management theories - Citing books, like "Five Dysfunctions of a Team" by Patrick Lencioni - Demonstrating theoretical understanding beyond practical experience - Showing continuous learning and professional development Communication and Execution - Strategic use of pauses to invite interviewer engagement - Natural tone and pacing for 45-minute interview format - Time management across multiple behavioral questions - Avoiding over-structured responses that feel artificial 👉 Book coaching or watch more mock interviews: https://www.interviewing.io 📝 Interview transcript & feedback: https://interviewing.io/mocks/fanng-behavioral-interview-2 🔗 Explore more FAANG interviews: https://interviewing.io/mocks?company=faang Disclaimer: All interviews are shared with explicit permission from the interviewer and interviewee. All candidates remain anonymous.

In this mock behavioral interview, a Principal Engineering Manager practices for a high level engineering manager role by discussing a complex technical project. The candidate walks through their experience managing a Virtual Agent Platform that handled customer support chatbots, focusing on a critical incident where 500K chat requests overwhelmed the system and how they implemented a session management solution to prevent duplicate escalations. 🧩 FAANG Behavioral Interview (Engineering Manager Role) The candidate managed a platform supporting customer support chatbots. When a major outage occurred, frustrated customers opened multiple chat sessions simultaneously, creating 20K requests per hour and overwhelming the downstream chat agent platform. The solution required implementing user session management with authentication, caching, and rate limiting while coordinating across multiple teams and maintaining customer experience. Chapters - 0:00 Introduction and candidate background - 1:32 Virtual Agent Platform overview and architecture - 15:18 The incident: 500K chat requests in 24 hours - 17:37 Solution approaches and tradeoffs - 28:50 Technical implementation with Redis caching - 36:34 Execution challenges and rollout strategy - 44:02 Interview feedback and improvements Concepts Platform Architecture & Integration - Multi-service architecture with load balancers and dispatchers - External platform dependencies and SDK integration - WebSocket connections for real-time chat sessions - Conversation tracking and session state management Incident Response & Problem Solving - Root cause analysis of system overload scenarios - Evaluating multiple solution approaches (client-side vs backend rate limiting) - Balancing immediate mitigation with long-term fixes - Fallback strategies and graceful degradation Authentication & Session Management - Redis caching for session state with TTL expiration - Token-based user identification across multiple browser tabs - Presence detection and connection management Cross-Team Coordination & Execution - Managing dependencies across multiple teams - Stakeholder alignment for 4-month delivery timeline - Rollout strategy across 32 international locales - Balancing technical constraints with business requirements 👉 Book coaching or watch more mock interviews: https://www.interviewing.io 📝 Interview transcript & feedback: https://interviewing.io/mocks/fanng-behavioral-interview-1 🔗 Explore more FAANG interviews: https://interviewing.io/mocks?company=FAANG Disclaimer: All interviews are shared with explicit permission from the interviewer and interviewee. All candidates remain anonymous.

In this behavioral mock interview for a major streaming company, a senior software engineer with 10 years of experience practices answering culture-focused questions for an L5 role. The interviewer, an experienced Netflix engineer, guides the candidate through key behavioral scenarios including cross-team collaboration, giving and receiving feedback, taking initiative, and making difficult team decisions. Watch as they discuss real examples from the candidate's work on migrating from external tools to internal systems, with detailed feedback on storytelling techniques and cultural fit. 🧩 The Focus: Netflix Behavioral Interview (L5 Senior Engineer) This behavioral interview focuses on Netflix's culture values including collaboration, candor, freedom and responsibility, and the keeper test. The candidate shares stories about leading cross-team migrations, handling difficult feedback situations, taking initiative on cost-saving projects, and contributing to team composition decisions. The interviewer provides real-time coaching on how to structure answers, balance technical details with behavioral insights, and demonstrate cultural alignment. Chapters - 0:00 Introduction and setup - 3:15 Cross-team collaboration question - 9:36 Feedback on collaboration answer - 15:15 Giving difficult feedback to colleagues and managers - 22:40 Receiving difficult feedback and self-awareness - 28:01 Freedom and responsibility - taking initiative - 42:5 The Keeper Test - team composition decisions - 50:49 General interview advice and cultural fit Concepts Behavioral Storytelling Techniques - Using the STAR method with specific examples - Balancing technical context with behavioral insights - Avoiding excessive jargon while maintaining credibility - Leading with business impact and cost savings Cross-Team Collaboration - Managing stakeholder resistance during migrations - Building consensus across multiple teams with different needs - Creating migration tools and testing strategies - Learning from failures and iterating on approach Feedback Culture and Candor - Establishing early feedback relationships with colleagues - Giving upward feedback to senior leadership effectively - Receiving criticism about communication style gracefully - Adapting feedback delivery to different audiences Initiative and Ownership - Identifying business problems proactively - Building compelling pitches with cost-benefit analysis - Taking ownership of end-to-end project outcomes - Demonstrating measurable impact on company metrics Team Composition and Performance - Contributing to performance review discussions - Making difficult decisions about team fit - Helping struggling team members find better roles - Balancing individual success with team effectiveness 👉 Book coaching or watch more mock interviews: https://www.interviewing.io 📝 Interview transcript & feedback: https://interviewing.io/mocks/netflix-behavioral-interview-1 🔗 Explore more Netflix interviews: https://interviewing.io/mocks?company=netflix Disclaimer: All interviews are shared with explicit permission from the interviewer and interviewee. All candidates remain anonymous.

We're back with another Discord event joined by none other than Austen McDonald, author of Master Behavioral Interviews: The Guide to Storytelling in Tech. Austen wrote THE book on behavioral interviews and was a Senior Software Engineering Manager at Meta, as well as a hiring committee chair there. This is the second time Austen has joined us, and this time, he will be answering questions from the audience as well. About the speaker: Austen is a seasoned engineering leader and former Senior Software Engineering Manager at Facebook/Meta. Over his 9+ years there, he served as hiring committee chair for iOS and Android pipelines, shaping hiring decisions, defining leveling standards, and mentoring interviewers. He is the author of Mastering Behavioral Interviews (out earlier this year). 🔎 What we cover: • How to confidently explain resume gaps — including layoffs — without hurting your chances • The subtle signals that show leadership potential (even as an intern) • Why memorizing “top behavioral questions” is the wrong way to prepare • A better approach: building and refining your core stories from real experience • How to structure answers so they’re clear, impactful, and actually memorable to interviewers • What hiring committees are really looking for when evaluating candidates And much much more! Timestamps: 00:00 Introduction 03:40 How to Explain Resume Gaps (Layoffs, Career Breaks) 06:55 How to Show Leadership as an Intern 10:50 Top Behavioral Interview Questions You Should Know 14:40 Best Way to Answer Behavioral Interview Questions (STAR Method) 19:07 Do Old Stories Hurt Your Interview? 21:40 How Long Should Interview Answers Be? 23:30 Balancing Technical vs Behavioral Interview Prep 25:34 How to Answer Conflict Questions (Even If You Avoid Conflict) 28:24 Best Strategies to Get Hired in Tech 30:59 Best Questions to Ask Your Interviewer 33:13 Should Engineers Prepare Questions for Interviews? 35:10 Senior vs Staff Engineer Behavioral Interview Differences 37:42 Do Behavioral Interviews Change by Company Size? 40:24 What to Do If You Can’t Think of an Example 44:04 How Interviewers Spot AI-Generated Answers 46:09 How to Answer “Why Did You Leave Your Job?” 47:28 How to Choose the Best Interview Stories 49:25 Live Q&A: Behavioral Interview Questions

In this mock machine learning system design interview, a former data scientist at a major tech company practices designing a large-scale video recommendation system while attempting to boomerang back to their former workplace as a software engineer. The interviewer, a former Google engineer, provides detailed feedback on ML system architecture, candidate generation vs ranking approaches, and the differences between L4 and L5 expectations at major tech companies. 🧩 The Problem: Design YouTube Recommendation System (Hard) Design a machine learning system for YouTube's home feed recommendations that maximizes user engagement through personalized video suggestions. The system must handle 2 billion users and 10 billion videos, serve recommendations within 1 second, and use a two-stage approach with candidate generation and ranking while considering scalability, feature engineering, and model training at massive scale. Chapters - 0:00 Introduction and candidate background - 4:16 Requirements gathering and business objectives - 11:40 Scale discussion and system constraints - 16:46 High-level architecture walkthrough - 28:23 Deep dive into feature engineering - 42:15 Video processing and embedding generation - 49:10 Training methodology and contrastive learning - 56:00 Final feedback and leveling assessment Concepts Requirements & Business Alignment - Translating business metrics (engagement) to ML objectives (CTR) - Defining recommendation scope (home feed vs watch next, long-form vs shorts) - Establishing baseline assumptions and data availability - Setting realistic latency and scale requirements Two-Stage ML Architecture - Candidate generation using approximate nearest neighbor (ANN) search - Ranking stage with detailed feature scoring - Feature stores for user and video embeddings - Flow from user query through retrieval to final recommendations Feature Engineering & Embeddings - Two-tower neural network architecture for user and video embeddings - Handling categorical variables through bucketing and embedding layers - Video processing using pre-trained models (ResNet, Vision Transformers) - Multimodal feature extraction from images, text, and audio Training & Model Optimization - Contrastive learning approaches - Positive/negative sample selection strategies - Batch processing and model quantization for inference speed - Offline computation and storage of video embeddings System Design & Serving - Embedding indices using locality-sensitive hashing - Feature store architecture for real-time lookups - Balancing compute costs with model complexity - End-to-end latency optimization strategies Interview Performance & Leveling - Importance of high-level architecture before diving deep - L4 vs L5 expectations for technical depth and fluency - Time management and component prioritization - Communication style and structured problem-solving approach 👉 Book coaching or watch more mock interviews: https://www.interviewing.io 📝 Interview transcript & feedback: https://interviewing.io/mocks/google-system-design-youtube-recommendation-system 🔗 Explore more Google interviews: https://interviewing.io/mocks?company=google Disclaimer: All interviews are shared with explicit permission from the interviewer and interviewee. All candidates remain anonymous.

In this mock coding interview, an experienced robotics engineer tackles a challenging variant of the classic "number of islands" problem, involving erosion simulation over time. Watch as they navigate the complexities of implementing algorithms in C++ within time constraints. 🧩 The Problem: Islands After K Days of Erosion (Medium) Given a 2D grid where 1 represents land and 0 represents water, find the number of islands remaining after K days of erosion. Each day, any land cell adjacent to water becomes water. The challenge involves efficiently calculating how long each land cell survives before being eroded, then counting connected components of cells that survive past K days. Chapters - 0:00 Introduction and candidate background - 3:41 Problem introduction and clarification - 6:04 Initial approach discussion - 15:55 Handling complex cases (islands with pools) - 19:09 Algorithm refinement and distance calculation - 27:23 Pseudocode planning - 39:08 C++ implementation - 51:10 Self-review and feedback discussion Concepts Problem Analysis & Approach - Recognizing the problem as a distance-from-shore calculation - Choosing BFS over DFS for layer-by-layer exploration - Handling edge cases like islands with internal water bodies Algorithm Design - Distance calculation using minimum adjacent visited cells plus one - BFS frontier exploration to process cells in correct order - Copying input to custom data structure for state tracking C++ Implementation Challenges - Defining custom structs to improve code readability over STL containers - Managing bounds checking with unsigned integers - Balancing code clarity with interview time constraints Interview Strategy & Communication - Taking time for thorough planning vs. rushing to code - Walking through examples to verify algorithm correctness - Self-awareness about pacing Time Management & Execution - Spending appropriate time on pseudocode before implementation - Recognizing when to focus on core algorithm vs. boilerplate code - Balancing thoroughness with interview time pressure 👉 Book coaching or watch more mock interviews: https://www.interviewing.io 📝 Interview transcript & feedback: https://interviewing.io/mocks/google-cplusplus-islands-after-k-days-of-erosion 🔗 Explore more Google interviews: https://interviewing.io/mocks?company=google Disclaimer: All interviews are shared with explicit permission from the interviewer and interviewee. All candidates remain anonymous.

In this mock ML system design interview, a software engineer with 7 years of experience practices designing a recommendation system for an upcoming interview. Watch as a Staff ML Engineer at Meta provides feedback on system design approach, highlighting the importance of structured thinking and end-to-end system perspective for ML engineering roles. 🧩 The Problem: Yelp Recommendation System (Medium) Design the machine learning recommendation algorithm behind Yelp's homepage that shows users personalized venue suggestions when they open the app. The system must optimize for user engagement and booking conversions while handling cold start problems, balancing exploration vs exploitation, and scaling to millions of users across different cities with varying venue availability. Chapters - 0:00 Introduction and interview context - 4:54 Problem clarification and business metrics - 10:25 End-to-end system flow design - 16:28 Data modeling and feature engineering - 25:13 Training pipeline and model architecture - 42:19 Self-evaluation and feedback - 44:22 Staff-level solution walkthrough - 52:54 Interview strategy and next steps Concepts Business Metrics & Product Scope - Balancing click-through rate with conversion optimization - Revenue maximization through booking commissions - User retention vs short-term engagement tradeoffs - Cold start handling for new users and venues System Architecture & Data Pipeline - Candidate generation through reverse indexing - Two-stage ranking with lightweight filtering and heavyweight scoring - Feature store design for offline and online features - Multi-task learning with separate prediction heads ML Modeling & Training - Two-tower architecture for user and venue embeddings - Pointwise learning-to-rank as binary classification - Handling class imbalance and negative sampling strategies - Position bias mitigation and popularity downweighting Production & Evaluation - A/B testing with interleaving experiments - Online metrics monitoring and data drift detection - Exploration vs exploitation through multi-armed bandits - MLOps pipeline with shadow deployment and rollback procedures Interview Communication & Structure - Following systematic ML system design framework - Balancing feature engineering depth with system completeness - Time management for end-to-end coverage - Demonstrating ML engineering vs data science perspective 👉 Book coaching or watch more mock interviews: https://www.interviewing.io 📝 Interview transcript & feedback: https://interviewing.io/mocks/meta-system-design-yelp-recommendations 🔗 Explore more Meta interviews: https://interviewing.io/mocks?company=meta Disclaimer: All interviews are shared with explicit permission from the interviewer and interviewee. All candidates remain anonymous.

In this mock system design interview, an L6/E6+ machine learning engineer builds an internal A/B experimentation platform for the likes of Amazon. The conversation focuses on clarifying requirements, designing routing and bucketing layers, modeling analytics, and balancing product extensibility with infrastructure fundamentals under pressure. 🧩 The Problem: A/B Experimentation Platform (Hard) Design an experimentation platform that lets internal teams split user traffic between control and treatment experiences, keep assignments consistent over time, collect outcome metrics, and support both classical experimentation and ML-driven optimization. Chapters - 10:08 Problem framing and requirement gathering - 15:30 Tradeoffs between pure experimentation and ML-driven adaptation - 24:01 API wrapper and routing model breakthrough - 31:35 Metrics storage strategy for analytics workloads - 44:06 Persisting user-bucket assignments and branch metadata - 48:03 Feedback on prioritization and time management - 53:33 Feedback on explaining the "why" behind clarifying questions Concepts Requirements & Scope - Functional and non-functional requirement decomposition - Idempotent user experience requirements - Defining control/treatment behavior and rollout flexibility System Architecture - API wrapper and request routing patterns - Hash-based bucketing and deterministic treatment assignment - Branch metadata and configuration management Data & Analytics Design - SQL-first modeling for OLAP-style analytics - Metrics aggregation strategy and treatment-vs-control comparison - BI dashboard needs and statistical significance considerations Scalability & Extensibility - Gradual traffic ramp-up (1% to 20% to 50%) - Read/write access patterns for experiment vs user-mapping data - Extending a platform for custom metrics and multi-armed bandit behavior Interview Execution & Communication - Agenda-setting to steer system design interviews - Time management tradeoffs between depth and design - Offering explicit reasoning for clarifying questions 👉 Book coaching or watch more mock interviews: https://www.interviewing.io 📝 Interview transcript & feedback: https://interviewing.io/mocks/faang-system-design-a-b-experimentation-platform 🔗 Explore more FAANG interviews: https://www.interviewing.io/mocks Disclaimer: All interviews are shared with explicit permission from the interviewer and interviewee. All candidates remain anonymous.

We’re joined by Marina Petrović, an ex-Google & ex-Meta recruiter who has reviewed thousands of technical candidates — and now helps engineers position themselves strategically in today’s competitive hiring market. In this live Discord event, Marina breaks down what actually matters in tech recruiting (and what’s just noise). We cover ATS myths, referrals, system design expectations, AI-assisted assessments, networking strategies, and what really happens after you “clear” the interview. If you’re aiming for FAANG-level roles — or trying to get back into interview shape — this is a practical deep dive into how hiring works from the inside. 🔎 What we cover: [00:00:00] Introduction and guest background [00:01:25] Debunking automated ATS scoring myths [00:03:36] The CODE method for behavioral interviews [00:06:40] Impact of referrals and seniority levels [00:10:45] Trends in AI-assisted coding assessments [00:13:12] Peak hiring months in tech [00:14:32] Networking strategies for entry-level candidates [00:16:50] Value of side projects and GitHub [00:19:15] Addressing significant career gaps [00:21:23] Beyond LeetCode: Communication and system design [00:22:32] Cold outreach to recruiters and managers [00:24:35] Navigating the Meta team matching phase [00:27:54] Networking for immigrants and unemployed candidates [00:30:03] Optimizing resumes for human recruiters [00:35:48] Reasons for rejection after clearing interviews [00:38:05] Professionalism and avoiding negative "bashing" [00:41:55] Getting interviews without a referral [00:46:58] Blacklists and rescheduling interview policies [00:50:00] Live audience Q&A session [01:04:02] Overview of Marina's one-on-one coaching [01:07:55] Closing remarks

A candidate works through a Leetcode Hard problem in Python during a mock interview with a senior FAANG engineer. The session covers problem clarification, edge cases, brute-force vs optimized solutions, in-place marking techniques, and time/space complexity tradeoffs. The interviewer gives feedback on pacing, simplicity, and responding to hints—exactly what other FAANG interviewers look for. 🧩 Problem — First Missing Positive Given an unsorted integer array nums. Return the smallest non-negative integer that is not present in nums. Key skills demonstrated • Clear problem understanding and proactive clarification of requirements • Strong communication and real-time explanation of thought process • Solid algorithmic reasoning, including time/space tradeoff analysis • Effective debugging and handling of edge cases under pressure 👉 Book coaching or watch more mock interviews: https://www.interviewing.io 📝 Interview transcript & feedback: https://interviewing.io/mocks/faang-python-first-missing-positive 🔗 Explore more FAANG interviews: https://www.interviewing.io/mocks Disclaimer: All interviews are shared with explicit permission from the interviewer and interviewee. All candidates remain anonymous.