Start by clarifying requirements and constraints, then outline a high-level design that separates concerns: task definition, scheduling algorithm, and execution. Dive into the core scheduling algorithm (e.g., priority queue or time wheel) and discuss trade-offs, scalability, and fault tolerance.
Pro tip: Demonstrate maturity by discussing how you would monitor and evolve the system over time, including metrics, logging, and handling of edge cases like task failures and retries.
Ask questions to understand the scope: what types of tasks (one-time, recurring), expected scale, latency requirements, and persistence needs. This shows you think before coding.
Sketch the main components: task producer, scheduler, executor, and storage. Explain how they interact and the flow of a task from submission to completion.
Choose and justify a data structure (e.g., min-heap for priority, time wheel for timers). Discuss how to handle concurrency, task dependencies, and fairness.
Address how the system scales (sharding, distributed queues) and ensures reliability (persistence, retries, idempotency, monitoring).
Summarize key trade-offs (e.g., throughput vs. latency) and suggest possible extensions like dynamic prioritization or backpressure.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Apparently one of the more common ones they throw at candidates.
Start by clarifying the problem: is it a shortest path, vehicle routing, or scheduling problem? Then model it as a graph and choose an appropriate algorithm (e.g., Dijkstra, A*, or DP for small constraints). Discuss trade-offs and test with edge cases.
Pro tip: Always ask about constraints (number of drivers, orders, time windows) before coding; this shows you prioritize understanding over jumping to code and helps you pick the right algorithm.
Ask about input size, time limits, whether routes are pre-defined, and if there are multiple drivers or time windows. This determines the algorithmic approach.
Represent locations as nodes and roads as weighted edges. Identify if it's a shortest path, TSP, or assignment problem. Define the objective (minimize time, distance, or cost).
Select an algorithm like Dijkstra for single-source shortest path, or DP with bitmask for small TSP. Use priority queues, adjacency lists, or matrices as needed.
Write clean code with helper functions. Test with simple cases, edge cases (no path, single node), and discuss time/space complexity.
If time permits, suggest optimizations (e.g., A* with heuristic, bidirectional search) and explain trade-offs between optimality and speed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the requirements and scope of the rules engine, such as the types of rules, expected scale, and integration points. Then, outline a high-level architecture, focusing on key components like rule parsing, evaluation, and conflict resolution, and discuss trade-offs between different design choices. Finally, walk through a concrete example to illustrate how the system would work end-to-end.
Pro tip: Demonstrate awareness of real-world constraints by discussing how you would handle rule versioning, testing, and gradual rollout to avoid breaking changes in production. This shows maturity beyond just theoretical design.
Ask questions to understand the scope: What types of rules (e.g., simple conditions, complex event processing)? What is the expected scale (rules count, evaluation frequency)? Who authors rules and how? What are latency and consistency requirements?
Identify essential parts: rule repository (storage), rule parser/compiler, evaluation engine, conflict resolution, and action executor. Consider whether rules are evaluated in real-time or batch.
Choose a rule format (e.g., DSL, decision tables, JSON) and evaluation strategy (e.g., forward chaining, Rete algorithm). Discuss how to handle complex conditions, priorities, and conflict resolution.
Explain how to scale the engine: caching, parallel evaluation, sharding, and optimizing rule matching. Discuss trade-offs between simplicity and performance.
Cover how the engine integrates with existing systems (APIs, event streams), versioning, testing, monitoring, and rollout strategies. Highlight trade-offs like consistency vs. availability.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is a new mandatory round and I was not expecting it.
Treat the AI assistant as a collaborative pair programmer: first restate the problem and clarify requirements, then use the AI to generate initial code or suggestions, but critically review and test every output. Narrate your reasoning, trade-offs, and how you validate the AI's contributions to demonstrate both technical judgment and adaptability.
Pro tip: Proactively mention that you verify AI-generated code with tests and edge cases, and that you keep the AI on a tight leash by giving it precise prompts—this shows you're in control, not the tool.
Restate the problem, ask clarifying questions, and outline your approach before touching the AI. Define inputs, outputs, edge cases, and constraints.
Use the AI to generate boilerplate, suggest algorithms, or draft initial code. Prompt it with specific, context-rich requests to get useful output.
Analyze the AI's suggestions for correctness, efficiency, and style. Identify potential bugs or edge cases the AI might have missed and fix them.
Write or run tests, including edge cases, to verify the solution. Use the AI to help generate test cases, but manually confirm the results.
Summarize your solution, highlighting why you accepted or rejected AI suggestions, and discuss any trade-offs (e.g., time vs. performance).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.