## Round 1 - Coding ## Problem Given a list of words and a maximum line width `W`, format the words so that each line is exactly `W` characters wide using full justification. Extra spaces are distributed as evenly as possible from left to right. The last line is left-justified. ```python def full_justify(words: list[str], max_width: int) -> list[str]: ... ``` ## Example ``` words = ["This","is","an","example","of","text","justification."] max_width = 16 Output: ["This is an", "example of text", "justification. "] words = ["What","must","be","acknowledgment","shall","be"] max_width = 16 Output: ["What must be", "acknowledgment ", "shall be "] ``` ## Follow-ups 1. How do you distribute spaces when the extra count is not divisible evenly by the number of gaps? 2. A line with a single word — how do you pad it correctly? 3. What changes if you must right-justify the last line instead of left-justify it? 4. How would you extend this to support unicode characters that may be multi-byte?

InterviewDB Experience · USA

Spaces Between Us: Justify Text to a Fixed Line Width

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Round 1 - Coding Problem Given a list of words and a maximum line width W, format the words so that each line is exactly W characters wide using full justification. Extra spaces are distributed as evenly as possible from left to right. The last line is left-justified. Example Follow-ups How do you distribute spaces when the extra count is not divisible evenly by the number of gaps? A line with a single word — how do you pad it correctly? What changes if you must right-justify the last line inste…

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About This Question

This is a candidate experience report from a glean interview during the phone round.

It covers the following topics: System Design, Coding, Phone, Onsite .

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System Design Coding Phone Onsite

About Glean Interview Reports

This question was reported by a candidate who interviewed at Glean. LeakCode aggregates interview reports from 10+ sources, including 1Point3Acres, Glassdoor, LeetCode Discuss, Blind, Reddit, Indeed, and Nowcoder. Each report is translated where necessary, deduplicated against existing entries, and tagged by company, role, round type, and reporting date.

Use this question as one calibration data point, not a memorization target. Companies typically rotate their question pools every 2-4 months; the exact wording of a 2024 question may differ from what you encounter today. The underlying pattern, difficulty level, and follow-up depth at Glean are the higher-signal extractions to take from this report.

For broader preparation context, the Glean interview process typically includes a recruiter screen, one or two technical phone screens, and a 4-5 round on-site loop covering coding, system design (at L4+ levels), and behavioral. Reports tagged on LeakCode show the round-by-round distribution and typical difficulty calibration. To browse questions filtered by round type and seniority, use the company hub linked above.

How To Practice This Type of Question

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.

Companies update their question pools every 2-4 months. The exact wording of any given question may have been retired by the time you interview. Focus your prep on the pattern, not the specific problem. The patterns that appear in Glean reports consistently are the ones worth investing in; one-off niche problems are not.

During Your Glean Round

Apply the standard interview round template: clarify requirements (2-3 minutes), state your approach out loud and confirm direction with the interviewer (3-5 minutes), code with narration (15-25 minutes), test with concrete examples including edge cases (5 minutes), discuss optimization or trade-offs if time permits (5 minutes). This template is universally accepted across FAANG and adjacent companies; deviating from it produces weaker interviewer feedback signal.

The single most predictive failure mode in Glean reports tagged "no hire": not asking clarifying questions. Interviewers are explicitly trained to weight this. Strong candidates ask 3-5 clarifying questions even on problems that look obvious; weak candidates dive into code immediately. The clarifying-question check is often the first signal recorded in the interviewer's written notes.