Axon·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

Jun 2026

Summary

Axon software engineer interview with a data structure design problem. The question was clean and well-scoped, though the O(1) constraint on getRandom is the part that trips people up if they haven't seen it before.

Questions Asked (1)

Design a data structure that supports insert, remove, and getRandom operations, all in average O(1) time. Each element in the set must have equal probability of being returned by getRandom.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

The insert and remove parts feel straightforward until you hit getRandom.

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AI HintsAI Generated

Suggested Approach

Combine a hash map for O(1) insert/remove with a dynamic array for O(1) random access. Store each element's index in the array within the hash map, and when removing, swap the target element with the last element in the array, then pop the last element and update the hash map accordingly.

Pro tip: Emphasize that the swap-with-last trick is what enables O(1) removal without shifting elements, and discuss how this maintains equal probability for getRandom. Also, mention edge cases like removing the last element or an element not present.

1. Clarify requirements and constraints

Confirm that all operations must be average O(1), that elements are unique (set semantics), and that getRandom must return each element with equal probability. Ask about potential duplicates or other constraints.

2. Propose the core data structures

Suggest using a hash map (dictionary) to store element-to-index mappings and a dynamic array (list) to store the elements. Explain that the array enables O(1) random access and the hash map enables O(1) lookups.

3. Detail insert and getRandom operations

For insert: check if element exists, append to array, and add index to hash map. For getRandom: generate a random index in the range of the array length and return the element at that index.

4. Explain remove with swap-and-pop

To remove an element: get its index from the hash map, swap it with the last element in the array, update the hash map for the swapped element, then remove the last element from the array and the element from the hash map.

5. Analyze complexity and edge cases

Discuss that all operations are average O(1) due to hash map operations and array indexing. Mention edge cases: removing the last element, removing an element not present, and handling empty set for getRandom.

Key Points to Mention

Hash map provides O(1) average time for insert, remove, and lookup.
Dynamic array provides O(1) random access for getRandom.
Swap-with-last technique avoids shifting elements, keeping removal O(1).
Updating the hash map after swapping is crucial to maintain correct indices.
Equal probability is guaranteed by uniform random index selection.
Edge cases: removing non-existent element, removing last element, and empty set.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.