Palo Alto Networks·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Jul 2026

Summary

Palo Alto Networks SWE interview with a fairly meaty algorithmic question that required more than just coding. They wanted the full picture: data structures, complexity, edge cases, the works.

Questions Asked (1)

Given two lists of strings, return all elements that appear in the first list but not the second, after normalizing both lists (e.g., lowercasing and trimming whitespace). Walk through your algorithm, data structure choices, duplicate handling, and time/space complexity.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

My first instinct was just to throw everything into a set and call it a day, but they kept pushing.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the normalization rules and duplicate handling, then propose a two-pass approach using a hash set for O(1) lookups. Walk through the algorithm step-by-step, analyze time and space complexity, and discuss trade-offs such as preserving order or handling large datasets.

Pro tip: Mention that you would confirm whether the output should preserve the original order of the first list and whether duplicates in the first list should be returned once or multiple times—this shows attention to detail and avoids incorrect assumptions.

1. Clarify requirements and edge cases

Ask about normalization rules (e.g., case sensitivity, trimming), duplicate handling, and whether order matters. Confirm input sizes to guide data structure choice.

2. Choose data structures

Use a hash set to store normalized elements from the second list for O(1) membership checks. Optionally use another set to track seen elements from the first list to handle duplicates.

3. Outline algorithm

Normalize and insert all elements of the second list into a set. Then iterate through the first list, normalize each element, and if it's not in the set and not already added, include it in the result.

4. Analyze complexity

Time complexity is O(n + m) where n and m are the lengths of the first and second lists, respectively. Space complexity is O(m + k) where k is the number of unique elements in the result.

5. Discuss trade-offs and optimizations

Mention alternatives like sorting both lists for O(n log n) time but O(1) extra space, or using a Bloom filter for approximate membership if memory is constrained. Also discuss handling of large datasets and streaming scenarios.

Key Points to Mention

Normalization: lowercasing and trimming whitespace, and possibly other Unicode considerations.
Duplicate handling: whether to return each occurrence or unique elements, and how to track seen elements.
Data structure choice: hash set for O(1) average-case lookups, and its impact on time/space complexity.
Time complexity: O(n + m) with hash set, and space complexity: O(m + k).
Order preservation: using a list to maintain order while using a set for deduplication.
Edge cases: empty lists, all elements matching, no matches, and large inputs.

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