Snowflake·Software Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say

May 2026Remote

Summary

Snowflake technical screen with a graph problem I wasn't fully prepared for. The constraint propagation angle made it trickier than a plain DAG traversal and I fumbled the conflict resolution part pretty badly.

Questions Asked (1)

You're given a DAG where each node has explicitly allowed and disallowed letter sets. Constraints propagate transitively from ancestors. For each node, compute the effective allowed and disallowed sets after full propagation, and explain how you'd handle conflicts where one ancestor allows a letter that another ancestor disallows. What's the time complexity?

Algorithms & Data StructuresTechnical Trade-offsSystem Design

Author's notes

I got the basic traversal down fast enough, topological sort and merge sets as you go.

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AI HintsAI Generated

Suggested Approach

Process nodes in topological order, merging each node's own sets with the effective sets of all its parents, then apply conflict resolution rules. For conflicts, define a clear policy (e.g., disallow wins) and explain the trade-offs. Analyze time complexity based on graph size and set operations.

Pro tip: Mention that using bitsets for letter sets can make union/intersection operations O(1) or O(letters/word_size), significantly improving performance for large graphs. Also, clarify that the conflict resolution policy should be consistent and may depend on domain requirements.

1. Clarify the problem and assumptions

Restate the problem: DAG with nodes having allowed/disallowed letter sets, constraints propagate transitively. Ask clarifying questions about conflict resolution (e.g., disallow overrides allow?) and letter set size.

2. Choose an algorithm

Use topological sort to process nodes in dependency order. For each node, compute effective sets by merging its own sets with the effective sets of all parents.

3. Define conflict resolution

Specify a policy: e.g., disallowed letters take precedence over allowed. Explain that this ensures safety and is common in constraint propagation.

4. Analyze time complexity

Time complexity is O(V + E) for topological sort plus O(E * L) for merging sets, where L is the number of letters. With bitsets, merging can be O(E * L / word_size).

5. Discuss optimizations and edge cases

Mention bitset representation, early termination if sets become empty, and handling of disconnected components or multiple roots.

Key Points to Mention

Topological sort for processing DAG in dependency order
Transitive propagation: each node's effective sets depend on all ancestors
Conflict resolution policy: disallow overrides allow (or vice versa) with justification
Time complexity: O(V + E + E*L) or O(V + E*L/word_size) with bitsets
Space complexity: O(V * L) for storing sets, or O(V * L/word_size) with bitsets
Edge cases: multiple parents, cycles (but DAG so none), empty sets, and large letter sets

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