## Problem Given a list of text documents and a stopword list, remove all stopword occurrences from each document. Matching is case-insensitive. Preserve original word casing for non-stopwords. Return the filtered documents. ```python def remove_stopwords( documents: list[str], stopwords: list[str] ) -> list[str]: pass ``` ``` Input: documents = ["The quick brown fox", "A dog and a cat"] stopwords = ["the", "a", "and"] Output: ["quick brown fox", "dog cat"] # Extra spaces collapsed, original case preserved for kept words. Input: documents = ["IS THIS WORKING"] stopwords = ["is", "this"] Output: ["WORKING"] ``` ## Follow-ups 1. What data structure do you use for stopword lookup, and why? 2. How do you handle punctuation attached to words (e.g., `"fox,"` where `"fox"` is a stopword)? 3. For a corpus of 10 million documents, how would you parallelize this pipeline? 4. Extend to support language-specific stopword lists that are selected based on detected document language.

InterviewDB Question

Stopword Filtering: Remove Common Words from Text Efficiently

phone

Question Details

Problem Given a list of text documents and a stopword list, remove all stopword occurrences from each document. Matching is case-insensitive. Preserve original word casing for non-stopwords. Return the filtered documents. Follow-ups What data structure do you use for stopword lookup, and why? How do you handle punctuation attached to words (e.g., "fox," where "fox" is a stopword)? For a corpus of 10 million documents, how would you parallelize this pipeline? Extend to support language-specific s…

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About This Question

This is a reported interview question from a perplexity interview during the phone round.

It covers the following topics: Coding, Phone .

Topics

Coding Phone

About Perplexity Interview Reports

This question was reported by a candidate who interviewed at Perplexity. 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 Perplexity are the higher-signal extractions to take from this report.

For broader preparation context, the Perplexity 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 Perplexity reports consistently are the ones worth investing in; one-off niche problems are not.

During Your Perplexity 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 Perplexity 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.