Anthropic·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Jun 2026

Summary

Got a coding question at Anthropic that's pretty much a classic file dedup problem. Nothing too exotic, but it requires you to think carefully about how you structure the grouping.

Questions Asked (1)

Given a list of directory path strings, where each string encodes a root directory and its files with their contents, find all groups of files that share identical content and return their full paths grouped together.

Algorithms & Data StructuresSystem Design

Author's notes

The parsing part is what trips people up first.

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AI HintsAI Generated

Suggested Approach

Clarify the input format and constraints, then design a solution that parses the directory strings into a file tree, computes a hash for each file's content, and groups files by their hash. Discuss trade-offs between hashing and direct comparison, and consider scalability for large datasets.

Pro tip: Mention that you would use a cryptographic hash like SHA-256 to minimize collision risk, but also discuss how to handle collisions by verifying with actual content comparison. This shows depth and awareness of real-world edge cases.

1. Clarify Input and Output

Ask questions to understand the exact format of the directory strings, how files and contents are encoded, and what the expected output structure is. Confirm edge cases like empty files or duplicate paths.

2. Parse and Build File Tree

Design a parser to extract file paths and contents from the strings, building a data structure (e.g., a map from file path to content) that represents all files.

3. Compute Content Hashes

For each file, compute a hash (e.g., SHA-256) of its content. Use a hash map to group file paths by their hash value.

4. Handle Collisions and Group

For each hash group, if there are multiple files, verify that contents are truly identical by comparing them directly (or by using a collision-resistant hash). Then output groups of identical files.

5. Analyze Complexity and Optimize

Discuss time and space complexity: O(N) for hashing, where N is total content size. Consider optimizations like streaming large files or using a rolling hash for incremental updates.

Key Points to Mention

Input parsing: handling nested directories and file contents from strings.
Hashing algorithm choice: SHA-256 for low collision probability, and collision resolution via direct comparison.
Data structures: hash map for grouping, and possibly a trie for efficient path storage.
Time and space complexity: O(N) time for hashing, O(N) space for storing contents and hashes.
Scalability: distributed hashing or MapReduce for very large datasets.
Edge cases: empty files, duplicate file paths, and files with identical content but different paths.

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