The opening question that basically ate the whole interview.
Start by clarifying functional and non-functional requirements, including scale, latency, and cost constraints. Then design the system in layers: data storage for word lists and templates, an asynchronous generation pipeline for puzzles, and a low-latency serving layer for interactive play. Emphasize trade-offs between generation complexity, caching, and user experience.
Pro tip: Treat puzzle generation as a batch/offline process with a job queue and precomputed cache, rather than generating on-demand per user. This avoids latency spikes and allows you to optimize the generator independently.
Ask about expected traffic, puzzle size, generation frequency, and whether puzzles are pre-generated or on-demand. Define core features: puzzle generation from word list and template, interactive solving, and job handling.
Sketch components: API gateway, puzzle service, generation service, job queue, cache, and database. Separate read-heavy serving from write-heavy generation to scale independently.
Design schemas for word lists, grid templates, generated puzzles, and user progress. Choose appropriate stores: e.g., relational DB for metadata, blob storage for large word lists, and Redis for session state.
Detail the generation algorithm (e.g., backtracking with heuristics), and how to run it asynchronously via a job queue (e.g., SQS, RabbitMQ). Discuss caching generated puzzles and handling failures/retries.
Explain how to serve puzzles with low latency using CDN and cache, handle concurrent users, and scale generation workers horizontally. Address monitoring, rate limiting, and cost optimization.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I knew backtracking was the right algorithm to mention but I fumbled the explanation of how you'd actually partition the work.
Start by clarifying the puzzle generation problem: what constitutes a puzzle, what is the search space, and what are the constraints (e.g., uniqueness, difficulty). Then propose a parallelization strategy that partitions the search space into independent chunks, assigns them to workers, and uses a coordinator to aggregate results and handle termination. Discuss trade-offs between static and dynamic partitioning, and how to ensure efficiency and load balancing.
Pro tip: Emphasize the importance of early termination and pruning: once a valid puzzle is found, workers should be able to stop or skip redundant work. Also, mention that the granularity of work units should be tuned to balance overhead and load imbalance.
Define what a puzzle is, the generation algorithm, and the size/structure of the search space. Identify constraints like uniqueness, difficulty, and any dependencies between puzzle components.
Decide between data parallelism (partition the search space) and task parallelism (different generation strategies). Consider using a master-worker pattern with a work queue for dynamic load balancing.
Split the space into independent chunks, e.g., by fixing certain parameters or using a hash-based partitioning. Ensure chunks are roughly equal in expected work and can be processed independently.
Use a coordinator to distribute work, collect results, and detect completion. Implement early termination: if a valid puzzle is found, signal workers to stop or skip redundant searches.
Tune work unit size, consider speculative execution, and handle failures/stragglers. Discuss trade-offs between static vs. dynamic partitioning, communication overhead, and scalability.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went straight to checkpointing and they seemed happy with that direction.
Start by clarifying the job's structure and failure semantics, then propose a design that checkpoints progress at a fine granularity and makes each unit of work idempotent. Explain how a crashed worker's job is detected and reassigned, and how the system resumes from the last checkpoint without redoing completed work.
Pro tip: Emphasize idempotency and exactly-once semantics—interviewers at OpenAI care about correctness under failure, not just recovery speed. Mention that you'd measure the cost of checkpointing versus recomputation to choose the right granularity.
Ask about the puzzle generation job's structure (e.g., monolithic vs. many small tasks) and what 'crash' means (process death, machine failure, network partition). Establish whether partial results are valid or must be discarded.
Propose breaking the job into small, independently retryable units of work. Each unit should be idempotent and write its result to durable storage (e.g., object store, database) with a unique key, so re-execution doesn't corrupt state.
Use a coordinator or lease-based system (e.g., heartbeats, timeouts) to detect crashed workers. Reassign orphaned work units to healthy workers, ensuring no two workers process the same unit simultaneously (e.g., via distributed locks or conditional writes).
On restart, the system should scan for incomplete work units and resume from the last durable checkpoint. Avoid starting over by tracking progress at the unit level and only redoing units that were in-flight or not yet started.
Explain trade-offs: checkpoint frequency vs. overhead, at-least-once vs. exactly-once semantics, and complexity of coordination. Mention monitoring for stuck jobs and alerting on repeated failures.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the workload characteristics and SLOs, then propose a decoupled architecture with a durable queue and autoscaling workers. Walk through how you'd handle bursts using queue depth-based scaling, backpressure, and prioritization, and discuss trade-offs like cost vs. latency.
Pro tip: Emphasize that you'd monitor queue depth and age, not just CPU, and use predictive scaling or scheduled scaling if bursts are predictable. Also mention the importance of idempotency and dead-letter queues to handle failures gracefully.
Ask about burst size, frequency, latency SLOs, cost constraints, and whether the load is predictable. This ensures your design aligns with actual needs.
Propose a durable, distributed queue (e.g., SQS, Kafka, RabbitMQ) to decouple producers from consumers. Discuss partitioning, priority queues, and dead-letter queues for poison messages.
Scale workers based on queue depth and message age, not just CPU. Use target tracking or step scaling policies, and consider predictive scaling for known bursts.
Implement backpressure by limiting queue size or rejecting requests when overwhelmed. Use rate limiting, circuit breakers, and graceful degradation to protect downstream services.
Set up monitoring for queue metrics, worker health, and end-to-end latency. Load test to validate scaling behavior and tune parameters based on real data.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
AC-3 came up and I was glad I remembered it from university.
Start by framing crossword filling as a constraint satisfaction problem (CSP) with variables (slots) and constraints (intersections). Then describe a backtracking search that uses constraint propagation (e.g., arc consistency) to prune the domain of possible words for each slot, and explain how this improves efficiency over naive brute force.
Pro tip: Mention that while constraint propagation reduces search space, it doesn't guarantee polynomial time; the problem remains NP-hard, so heuristics like MRV (minimum remaining values) and LCV (least constraining value) are crucial for practical performance.
Define variables as word slots (across and down) and domains as all dictionary words of the required length. Constraints are the intersections: letters at overlapping positions must match.
Use arc consistency (e.g., AC-3) to remove words from domains that have no support in intersecting slots. This prunes the search space before and during search.
Perform backtracking search, selecting the unassigned slot with the fewest remaining words (MRV) and trying words that least constrain neighbors (LCV). After each assignment, re-run propagation.
If a domain becomes empty, backtrack to the previous assignment and try the next option. Continue until all slots are filled or all possibilities exhausted.
Discuss optimizations like maintaining arc consistency (MAC), using a trie for fast word lookup, and the trade-off between propagation strength and computational cost.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Ran through a relational model for puzzles and attempts, with a separate key-value store for live session state.
Start by clarifying the system's requirements and scale, then walk through the core entities (puzzle, user attempt) and their relationships. Explain your storage choices (e.g., SQL vs NoSQL) based on access patterns, consistency needs, and scalability, and discuss how you'd evolve the model over time.
Pro tip: Show awareness of trade-offs by explicitly stating why you chose a particular storage solution over alternatives, and mention how you'd handle schema migrations or versioning as the puzzle format evolves.
Ask about expected read/write patterns, data volume, latency requirements, and consistency needs to ground your design in real constraints.
Identify the puzzle entity (e.g., id, type, content, solution, metadata) and user attempt state (e.g., user_id, puzzle_id, progress, timestamps), and describe how they relate.
Select a storage solution (e.g., relational for structured data, document store for flexible schemas, or a combination) and justify it based on access patterns and scalability.
Explain how you'll query the data efficiently (e.g., fetching a puzzle by id, updating attempt state) and what indexes or denormalizations you'd use.
Cover how the model can evolve (e.g., new puzzle types, versioning) and the trade-offs of your choices (e.g., consistency vs availability, normalization vs performance).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Covered load puzzle, submit guess, and validate endpoints.
Start by clarifying the requirements and constraints of the puzzle-playing experience, such as the types of puzzles, real-time interactions, and client platforms. Then propose a RESTful API for core operations and a WebSocket-based API for real-time updates, ensuring scalability and security. Finally, discuss how the API design supports extensibility and integration with OpenAI's models.
Pro tip: Emphasize idempotency and versioning in your API design to ensure reliability and backward compatibility, which is crucial for a platform like OpenAI that serves diverse clients.
Ask questions to understand the scope: what types of puzzles, expected load, real-time needs, and client platforms. This shows you prioritize understanding before designing.
Identify key entities like puzzles, sessions, moves, and hints. Outline CRUD operations and actions such as starting a session, submitting a move, and requesting a hint.
Choose appropriate protocols: REST for stateless operations (e.g., fetching puzzle details) and WebSockets for real-time interactions (e.g., live updates during a game). Consider GraphQL for flexible querying if needed.
Discuss scalability, security (authentication, rate limiting), idempotency, versioning, and error handling. Explain how the API supports these aspects.
Explain how the API can integrate with OpenAI's models for features like hint generation or difficulty adjustment, and how it can be extended for new puzzle types.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.