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F5 Networks·Mobile Engineer·Online Assessment (OA)·Intermediate

Intermediate
Apr 2026

Summary

F5 Networks OA for a Mobile Engineer role, one coding question, 40 minutes on the clock. Pretty straightforward setup but the problem itself had some moving parts worth thinking through.

Questions Asked (1)

Q1

You're given two arrays of length n: one with tweet content as strings, one with publication timestamps as integers. You're also given a current time and a time window. Find the top 3 most popular hashtags among tweets published within [currentTime - timeWindow, currentTime].

Algorithms & Data Structures
Author's notes

Spent the first few minutes just parsing what they were actually asking.

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AI HintsAI Generated

Suggested Approach

First, filter tweets by timestamp within the window, then extract and count hashtags from the filtered tweets. Finally, return the top 3 hashtags by frequency, handling ties and edge cases.

Pro tip: Clarify assumptions upfront: whether hashtags are case-sensitive, how to handle ties, and if the input arrays are sorted by time. This shows attention to detail and avoids incorrect assumptions.

1. Clarify requirements and edge cases

Ask about hashtag definition (e.g., case sensitivity, valid characters), tie-breaking rules, and input constraints (e.g., n size, time range).

2. Filter tweets by time window

Iterate through timestamps and select tweets where timestamp is between currentTime - timeWindow and currentTime, inclusive.

3. Extract and count hashtags

For each filtered tweet, parse hashtags (e.g., using regex or split) and increment a frequency map (hash map or dictionary).

4. Find top 3 hashtags

Use a min-heap of size 3 or sort the frequency map to get the top 3 hashtags by count, applying tie-breaking rules if specified.

5. Analyze complexity and optimize

Discuss time and space complexity (O(n + m) where m is total hashtags) and mention possible optimizations like early filtering or streaming if data is large.

Key Points to Mention

  • Time complexity: O(n + m) where n is number of tweets and m is total hashtags extracted.
  • Space complexity: O(k) where k is number of unique hashtags, plus O(1) for top 3 heap.
  • Use of hash map for frequency counting and min-heap for top K elements.
  • Handling ties: specify if alphabetical order or first occurrence is used.
  • Edge cases: no tweets in window, fewer than 3 unique hashtags, empty hashtags.
  • Hashtag parsing: consider regex like /#\w+/ and case sensitivity.

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