## Problem You are given a list of content items, each with a `type` ("article", "ad", "sponsored"), `source`, and `score`. Filter out items that are ads or sponsored, and also remove organic items with a score below a minimum threshold. Return the remaining items sorted by score descending. ```python from dataclasses import dataclass @dataclass class ContentItem: id: int type: str source: str score: float def filter_ads( items: list[ContentItem], min_score: float ) -> list[ContentItem]: pass ``` ``` Input: items = [ ContentItem(1, "article", "cnn", 0.9), ContentItem(2, "ad", "google", 0.95), ContentItem(3, "sponsored", "brand", 0.7), ContentItem(4, "article", "bbc", 0.3), ] min_score = 0.5 Output: [ContentItem(1, "article", "cnn", 0.9)] # item 2,3 removed (ad/sponsored), item 4 below threshold ``` ## Follow-ups 1. How would you extend the filter to use a blocklist of known ad sources? 2. If `type` is missing or null, what fallback heuristic would you apply? 3. How would you add a user-configurable filter pipeline where each rule is a pluggable strategy? 4. Extend to support "native ads" that look like articles — what signals would you use to detect them?

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Ads Filter: Remove Advertisements from a List Based on Heuristics

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Problem You are given a list of content items, each with a type ("article", "ad", "sponsored"), source, and score. Filter out items that are ads or sponsored, and also remove organic items with a score below a minimum threshold. Return the remaining items sorted by score descending. Follow-ups How would you extend the filter to use a blocklist of known ad sources? If type is missing or null, what fallback heuristic would you apply? How would you add a user-configurable filter pipeline where each…

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About This Question

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

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

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About Faire Interview Reports

This question was reported by a candidate who interviewed at Faire. 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.

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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 Faire reports consistently are the ones worth investing in; one-off niche problems are not.

During Your Faire 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 Faire 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.