Google Cloud (GCP)·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Jul 2026

Summary

Google Cloud SWE interview with a single coding problem centered on a Rummy hand. Not a lot of context given in the original so hard to say how it went, but the problem itself is more involved than it first looks.

Questions Asked (1)

Given a 12-card Rummy hand, determine whether it forms a valid Rummy solution (i.e., can be arranged into valid sets and runs).

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

This one takes a bit to wrap your head around.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Clarify the rules of Rummy (e.g., set size, run length, ace handling) and then model the problem as a search/backtracking or dynamic programming problem. Discuss how to efficiently determine if the hand can be partitioned into valid sets and runs, considering constraints and potential optimizations.

Pro tip: Mention that the problem is NP-complete in general but for a fixed hand size (12 cards) a backtracking solution with pruning is feasible; also discuss how to handle edge cases like duplicate cards and jokers.

1. Clarify Rules and Constraints

Ask about the specific Rummy variant: minimum set size (usually 3), minimum run length (usually 3), whether jokers are allowed, and ace high/low. Confirm the input format (e.g., list of cards with suit and rank).

2. Define Valid Groups

Define what constitutes a valid set (same rank, different suits) and a valid run (consecutive ranks of the same suit). Consider all possible groupings of the 12 cards.

3. Choose an Algorithm

Propose a backtracking approach: try to form a valid group, remove those cards, and recursively check the remainder. Alternatively, use dynamic programming with bitmasking to represent subsets of cards.

4. Optimize and Prune

Discuss pruning strategies: sort cards, avoid duplicate groupings, and use memoization to cache failed subsets. For 12 cards, the search space is small enough for backtracking with pruning.

5. Analyze Complexity and Trade-offs

Analyze time and space complexity. Mention that the problem is NP-complete in general, but for fixed hand size it's constant time. Discuss trade-offs between exhaustive search and heuristic approaches.

Key Points to Mention

Backtracking with recursion and pruning
Bitmask dynamic programming for subset representation
Handling jokers/wildcards as flexible cards
Time complexity: O(2^n) worst-case but n=12 is small
Edge cases: duplicate cards, invalid inputs, ace high/low
Comparison with greedy approaches and why they fail

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