Google·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

May 2026

Summary

Google SWE coding round, one algorithmic problem about grouping two-digit integers by their digit composition. Pretty focused session, just the one problem with some back-and-forth on the approach.

Questions Asked (1)

Given an array of two-digit integers (10-99), group numbers that share the same digits regardless of order. Return the size of the largest such group. For example, 12, 21, and any other permutation of those digits belong together.

Algorithms & Data Structures

Author's notes

The example they gave makes it click pretty fast: 12 and 21 are the same group, 34 and 43 are the same group.

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AI HintsAI Generated

Suggested Approach

Clarify that the input is an array of two-digit integers and we need to group numbers that are permutations of each other. Use a canonical key for each number, such as sorting its digits, and count frequencies with a hash map to find the largest group size.

Pro tip: Mention that since numbers are two-digit, you can also use a fixed-size array of size 100 for counting, but a hash map is more general and still O(n). Also, discuss edge cases like empty input or numbers with repeated digits (e.g., 11).

1. Understand the problem

Restate the problem: group two-digit numbers by their digit multiset and return the size of the largest group. Confirm that order of digits doesn't matter.

2. Choose a canonical representation

For each number, extract its tens and ones digits, sort them, and form a key (e.g., a string or a tuple). This key uniquely identifies the digit set.

3. Count frequencies

Iterate through the array, compute the key for each number, and increment its count in a hash map. Track the maximum count seen so far.

4. Return the result

After processing all numbers, return the maximum frequency. If the array is empty, return 0.

5. Analyze complexity

Time complexity is O(n) since each number is processed in constant time. Space complexity is O(k) where k is the number of distinct digit sets, at most 45 for two-digit numbers.

Key Points to Mention

Canonical key: sorting the digits of each number to create a unique identifier for the group.
Hash map for counting frequencies: efficient O(1) average time per insertion/lookup.
Edge cases: empty array, numbers with repeated digits (e.g., 11, 22), and numbers like 10 (digits 1 and 0).
Time and space complexity analysis: O(n) time, O(k) space where k ≤ 45.
Alternative approach: using a fixed-size array of size 100 (since numbers are 10-99) to count directly, but hash map is more general.
Clarify assumptions: input is an array of integers, not necessarily sorted, and we only need the size of the largest group, not the groups themselves.

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