## Round 1 - Coding ## Problem Given a block of text, compute the frequency distribution of word lengths. Return a sorted list of `(length, count)` pairs and render a simple ASCII histogram. ```python def word_length_distribution(text: str) -> list[tuple[int, int]]: # returns [(length, count), ...] sorted by length ascending ... def render_histogram(distribution: list[tuple[int, int]]) -> str: # each row: "length | ##### count" ... ``` ## Example ``` text = "the quick brown fox jumps over the lazy dog" word_length_distribution(text) # -> [(3,4),(4,2),(5,2),(6,1)] # "the"x2, "fox", "the" -> len 3 appears 4 times # "over","lazy" -> len 4 appears 2 times, etc. render_histogram([(3,4),(4,2),(5,2)]) # 3 | #### 4 # 4 | ## 2 # 5 | ## 2 ``` ## Follow-ups 1. What counts as a "word"? How do you handle punctuation attached to words like `"fox,"` or `"dog."`? 2. How would you normalize the histogram so bars represent percentages instead of raw counts? 3. How does the output change for very long words (length 20+) — does your histogram stay readable? 4. Extend this to also output the mean, median, and mode word length.

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Word Length Distribution: Compute and Display the Frequency Distribution of Word Lengths

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Round 1 - Coding Problem Given a block of text, compute the frequency distribution of word lengths. Return a sorted list of (length, count) pairs and render a simple ASCII histogram. Example Follow-ups What counts as a "word"? How do you handle punctuation attached to words like "fox," or "dog."? How would you normalize the histogram so bars represent percentages instead of raw counts? How does the output change for very long words (length 20+) — does your histogram stay readable? Extend this to…

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Solve similar problems on LeetCode under timed conditions (25-35 minutes per medium difficulty). The goal is pattern recognition: recognize the underlying technique (sliding window, two-pointer, BFS, memoized recursion, etc.) within 60-90 seconds of reading. Strong candidates verbalize their hypothesis out loud before coding, then iterate based on feedback. Weak candidates dive into implementation immediately, lose time on the wrong approach, and run out of time for follow-ups.

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