Model the grid as a graph where each cell is a node and edges connect adjacent non-obstacle cells. Use BFS from the start to compute the shortest path to the target food cell, returning the distance or -1 if unreachable.
Pro tip: Clarify assumptions upfront: whether diagonal moves are allowed, if multiple foods exist, and if the grid can be modified. This shows attention to detail and prevents misalignment with the interviewer.
Ask about movement rules (4-directional vs. 8-directional), grid size limits, and whether the start or food can be on obstacles. Confirm the return value for unreachable cases.
Explain that BFS is optimal for unweighted grids because it explores level by level, guaranteeing the shortest path. Mention that DFS would not guarantee shortest path.
Initialize a queue with the start cell and a visited set. While the queue is not empty, dequeue a cell, check if it's the target, and enqueue all valid unvisited neighbors.
Discuss handling of invalid start/target, obstacles, and grid boundaries. State time and space complexity: O(R*C) for both, where R and C are grid dimensions.
Walk through a small example to verify the algorithm, including a case where the target is unreachable, and confirm the output.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Model the problem as a shortest path on a state space where each state is (current position, set of collected food items). Use BFS to compute distances between all points of interest (start and food items), then apply dynamic programming (TSP-style) to find the optimal order to collect all food items. The complexity is O((F+1)^2 * 2^F) where F is the number of food items (≤12).
Pro tip: Emphasize that BFS on the grid gives unweighted shortest paths, and the DP over subsets is feasible because F ≤ 12, making 2^F manageable. Also, mention that you can optimize by precomputing distances only between points of interest, not the entire grid.
Treat the start S and each food item as nodes. Run BFS from each node to compute the shortest distance to every other node, considering only walkable cells.
Let dp[mask][i] be the minimum steps to collect the set of food items represented by mask and end at food item i. Initialize dp[1<<i][i] = dist[S][i] for each food i.
For each mask and last food i, try adding an uncollected food j: dp[mask | (1<<j)][j] = min(dp[mask][i] + dist[i][j]). Iterate masks in increasing order.
The answer is min over i of dp[(1<<F)-1][i]. Time complexity: O(F * 2^F * F) = O(F^2 * 2^F) for DP, plus BFS: O(F * R*C). Space: O(2^F * F) for DP.
Write code for BFS and DP, ensuring correct handling of unreachable food items (return -1 or infinity). Test with small cases and edge cases like no food items.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Felt more like a system design question dressed up as a coding one.
Start by clarifying requirements and assumptions, then outline a data model and processing logic. Discuss trade-offs and edge cases, and finally sketch a scalable implementation.
Pro tip: Emphasize idempotency and exactly-once processing, as financial systems require handling duplicate or out-of-order events reliably.
Ask about event ordering, duplicate handling, and expected scale to define the problem scope.
Define an Order struct with fields like order_id, total_quantity, filled_quantity, and status.
Describe how to update order state based on event type: NEW creates order, FILL increments filled_quantity, CANCEL marks as cancelled.
Discuss handling of duplicate events, out-of-order events, partial fills, and cancellations after fills.
Mention partitioning by order_id, using in-memory state with persistence, and potential for distributed processing.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Kept a set of seen eventIds and skipped any event whose id was already in it.
Start by clarifying the delivery semantics and the need for idempotency, then propose a deduplication mechanism using a persistent store keyed by eventId, and discuss trade-offs like storage cost, latency, and failure scenarios. Emphasize that idempotency should be enforced at the processing layer, not just at the ingestion layer.
Pro tip: Mention that you would also consider idempotent side effects (e.g., using upserts or conditional writes) to handle cases where deduplication fails, and highlight the importance of monitoring duplicate rates to detect issues early.
Ask about the expected duplicate rate, latency requirements, and whether the event processor is stateful or stateless. This shows you understand the problem context before jumping to solutions.
Propose using a persistent store (e.g., Redis, DynamoDB) to track processed eventIds with a TTL. Discuss alternatives like in-memory caches (risky for distributed systems) or database unique constraints.
Outline a flow: check if eventId exists in the store; if not, process the event and atomically record the eventId. Ensure atomicity using transactions or conditional writes to avoid race conditions.
Explain what happens if the store is unavailable or if the process crashes after processing but before recording. Suggest idempotent side effects (e.g., upserts) and retry mechanisms with exponential backoff.
Cover storage cost vs. deduplication window, latency impact, and scalability. Mention partitioning by eventId to distribute load and using bloom filters for memory efficiency if appropriate.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
O(E) time, O(E + O) space where E is events and O is distinct orders.
First, clearly define the variables: let N be the number of events and M be the number of distinct orders. Then, walk through your solution's data structures and algorithms, deriving time and space complexities in terms of N and M, and explain how they scale. Finally, discuss any trade-offs or optimizations you considered.
Pro tip: Explicitly state the assumptions about the input (e.g., events are streamed or batched, order IDs are unique) because they affect the complexity analysis. Also, mention that in practice, M is often much smaller than N, so focusing on N is key.
State that N is the number of events and M is the number of distinct orders. Clarify whether events are processed in a stream or in batch, and whether order IDs are unique.
Explain the key data structures (e.g., hash map for order state, queue for events) and how they store data relative to N and M.
Break down the time complexity per event and overall. For example, O(1) per event for hash map updates, leading to O(N) total time, and mention any operations that depend on M.
Determine the space used by data structures. For example, O(M) for storing order states and O(N) if storing all events, or O(1) extra if streaming.
Mention any trade-offs (e.g., time vs. space) and potential optimizations, such as using more efficient data structures or parallel processing.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.