# 4z7l/tech_interview.zip

✅ 취준하면서 모았던 면접 질문 모음집 ✅

Repository: https://github.com/4z7l/tech_interview.zip
Canonical: https://ross.abutalabs.com/products/tech_interviewzip
License Family: other
Last push: 2021-09-29T01:04:05+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 1868, "days_push": 1800, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2226, forks 165 (observed 2026-08-28T04:06:28.727180+00:00)

## What it is
A curated Korean-language collection of technical and behavioral interview questions gathered during a job search, covering topics like C++, Java, algorithms, databases, networking, and operating systems. It is a static study reference, also viewable on Notion, rather than runnable software.

## Use cases
- prepare for software engineer technical interviews
- find common behavioral interview questions
- review CS fundamentals like OS and networking before interviews
- practice hand-written coding interview problems
- study interview questions for Korean IT companies like Naver

## When to choose
- you are preparing for tech interviews, especially in the Korean job market
- you want a broad question bank spanning CS fundamentals and languages
- you prefer reading curated question lists over interactive courses

## When to avoid
- you need runnable code, tools, or libraries
- you want up-to-date content - the repo was last updated in 2021
- you need English-language interview material

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: documentation
- domain: tutorials, developer-tools, education
- platform: cross-platform
- tags: interview-preparation, tech-interview, korean, study-notes, job-hunting

## Member repositories
- 4z7l/tech_interview.zip (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:28.727180+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T02:44:52.287957+00:00, confidence not recorded.
  - readme: https://github.com/4z7l/tech_interview.zip (fetched 2026-08-28T04:06:28.727180+00:00, sha 6508442007c1)
- Data as of 2026-08-30T08:39:29.467469+00:00.