Start by clarifying the requirements (e.g., capacity, thread-safety, eviction policy) and then propose a design using a hash map and a doubly linked list to achieve O(1) operations. Explain how the hash map provides direct access to nodes while the linked list maintains the recency order, and then walk through the implementation details for get and put.
Pro tip: Mention edge cases like updating an existing key, handling capacity 0 or 1, and discuss potential concurrency issues if the cache needs to be thread-safe. This shows attention to detail and real-world considerations.
Ask questions to understand constraints: expected capacity, whether thread-safety is required, and if the eviction policy is strictly LRU. This ensures you design the right solution.
Select a hash map for O(1) key lookup and a doubly linked list to track usage order. The hash map stores key-node pairs, and the linked list maintains nodes in order from most to least recently used.
For get: if key exists, move its node to the front of the list and return the value. For put: if key exists, update value and move to front; if not, create a new node, add to front, and if capacity is exceeded, remove the tail node and delete its key from the map.
Write clean code with helper functions for adding to front and removing nodes. Test with scenarios like repeated gets, puts that trigger eviction, and updating existing keys.
Mention alternative approaches (e.g., using an ordered dictionary) and their trade-offs. Also discuss potential improvements like thread-safety using locks or concurrent data structures.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is basically asking for a random derangement.
Start by clarifying requirements and edge cases, then outline a randomized derangement algorithm (e.g., Fisher-Yates shuffle with rejection or cyclic shift) that guarantees no self-assignment. Discuss validation, error handling, and how to test the solution.
Pro tip: Mention that you would validate the assignment (e.g., check no self-gifts and all participants included) and consider scalability for large CSVs, showing you think about robustness and performance.
Ask about CSV format, number of participants, handling of duplicates, and whether the assignment must be uniformly random. Confirm that every person gives and receives exactly once.
Select an approach like Fisher-Yates shuffle with rejection (if self-assignment occurs) or a cyclic shift with random rotation. Ensure the algorithm is efficient and unbiased.
Write code to read the CSV, extract participant names, apply the algorithm, and output the assignments. Include error handling for invalid input.
Verify that no one is assigned to themselves and that all participants are included exactly once. Test with edge cases like 2 participants, large lists, and malformed CSV.
Explain how the solution scales and whether the randomization is uniform. Mention potential improvements like using cryptographic randomness if needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.