Apple·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

May 2026

Summary

Apple SWE interview that centered almost entirely on one meaty algorithm problem. The question had a lot of layers and the follow-ups kept coming, which I wasn't fully prepared for.

Questions Asked (1)

Given an array or stream of integers and a value k, return any valid ordering of the k most frequently occurring integers. Implement a heap-based solution, then walk through how you'd handle ties, negative numbers, very large inputs, and streaming data under memory constraints. Also analyze time and space complexity and describe a bucket-based alternative.

Algorithms & Data StructuresTechnical Trade-offsSystem Design

Author's notes

This felt like one question that kept growing arms.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and edge cases, then present a heap-based solution using a frequency map and a min-heap of size k. Walk through tie-breaking, negative numbers, large inputs, and streaming constraints, analyzing time/space complexity and comparing with a bucket-based alternative.

Pro tip: Emphasize that tie-breaking is arbitrary unless specified, and in streaming scenarios, use a space-saving algorithm like Misra-Gries or count-min sketch for approximate heavy hitters. This shows awareness of real-world constraints and trade-offs.

1. Clarify requirements and edge cases

Ask about input size, memory limits, whether ties matter, and if the stream is static or dynamic. Confirm expected output format (any valid ordering).

2. Present heap-based solution

Build a frequency map, then use a min-heap of size k to keep the k most frequent elements. For streaming, maintain counts and update heap incrementally.

3. Address edge cases and constraints

Discuss handling ties (arbitrary or stable), negative numbers (hash map handles them), large inputs (external sorting or distributed counting), and streaming under memory constraints (approximate algorithms).

4. Analyze complexity and compare alternatives

State time O(n log k) and space O(n) for heap approach. Describe bucket-based alternative: O(n) time and space by bucketing frequencies, but requires knowing max frequency.

Key Points to Mention

Frequency map (hash map) to count occurrences, handling negative numbers naturally.
Min-heap of size k for efficient top-k selection, with time O(n log k) and space O(n).
Tie-breaking: if frequencies equal, any order is valid; if stability needed, use secondary key.
Streaming under memory constraints: use approximate algorithms like Misra-Gries or count-min sketch, or maintain a fixed-size heap with periodic pruning.
Bucket-based alternative: O(n) time and space by grouping elements by frequency, but requires two passes and known max frequency.
Trade-offs: heap is simple and works for any k; bucket is faster but less flexible for streaming or unknown ranges.

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