
ai-tech-interview
๐ฉโ๐ป๐จโ๐ป AI ์์ง๋์ด ๊ธฐ์ ๋ฉด์ ์คํฐ๋ (โญ๏ธ 1k+)
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This repository contains a collection of interview questions related to various topics such as statistics, machine learning, deep learning, Python, networking, operating systems, data structures, and algorithms. The questions cover a wide range of concepts and are suitable for individuals preparing for technical interviews in the field of artificial intelligence and data science.
README:
[!WARNING]
PR ์์ฒญ ์ ์์ฑ ๊ท์น์ ์ค์ํด์ฃผ์ธ์. ์ค์ํ์ง ์์ ์ ํด๋น PR์ ๊ฑฐ์ ๋ ์ ์์ต๋๋ค.
- ํผ๋๋ฐฑ์ Pull Request๋ฅผ ํตํ ํผ๋๋ฐฑ ์์ฒญ ๋ฐฉ๋ฒ์ ์ฐธ๊ณ ํ์ฌ Pull Request๋ก ๋ณด๋ด์ฃผ์ธ์.
- Pull Request ์์ฑ ๊ท์น์ ์ฌ๊ธฐ๋ฅผ ์ฐธ๊ณ ํด์ฃผ์ธ์.
- GitHub ์ธ์ GitBook ์ฌ์ดํธ๋ก๋ ๋ณด์ค ์ ์์ต๋๋ค.
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ํ์ง๋ง Latex ๋ฌธ๋ฒ์ด ๋ฌ๋ผ ๋ค๋ฅธ ์น์ฌ์ดํธ๋ก ๋ง์ด๊ทธ๋ ์ด์ ์์ ์ ๋๋ค.์๊ฐ๋ ๋ ๋ง์ด๊ทธ๋ ์ด์ ์์ ์ ๋๋ค..๐ฅฒ
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- ๊ถ๊ธํ ์ ์ด ์๊ฑฐ๋ ๊ณต์ ํ๊ณ ์ถ์ ํ์ด ์์ผ์๋ฉด Discussion์ ํ์ฉํด์ฃผ์ธ์.
- ์ปค๋ฎค๋ํฐ ํ์ฑํ๋ ์ธ์ ๋ ์ง ํ์์ ๋๋ค!
- ๋ฉด์ ๋ ํฌ ๊ฐ์ ํ๋ก์ ํธ ์งํ ์ํฉ์ ์ฌ๊ธฐ๋ฅผ ํ์ธํด์ฃผ์ธ์.
- ๊ณต์ง์์ ๋ง์๋๋ฆฐ ๊ฒ์ฒ๋ผ ์งํ์ด ๋๋ ์ ์์ต๋๋ค.
๐ ํต๊ณ/์ํ
- ๊ณ ์ ๊ฐ(eigen value)์ ๊ณ ์ ๋ฒกํฐ(eigen vector)์ด ๋ฌด์์ด๊ณ ์ ์ค์ํ์ง ์ค๋ช ํด์ฃผ์ธ์.
- ์ํ๋ง(Sampling)๊ณผ ๋ฆฌ์ํ๋ง(Resampling)์ด ๋ฌด์์ด๊ณ ๋ฆฌ์ํ๋ง์ ์ฅ์ ์ ๋ง์ํด์ฃผ์ธ์.
- ํ๋ฅ ๋ชจํ๊ณผ ํ๋ฅ ๋ณ์๋ ๋ฌด์์ธ๊ฐ์?
- ๋์ ๋ถํฌ ํจ์์ ํ๋ฅ ๋ฐ๋ ํจ์๋ ๋ฌด์์ธ๊ฐ์? ์์๊ณผ ํจ๊ป ํํํด์ฃผ์ธ์.
- ์กฐ๊ฑด๋ถ ํ๋ฅ ์ ๋ฌด์์ธ๊ฐ์?
- ๊ณต๋ถ์ฐ๊ณผ ์๊ด๊ณ์๋ ๋ฌด์์ผ๊น์? ์์๊ณผ ํจ๊ป ํํํด์ฃผ์ธ์.
- ์ ๋ขฐ ๊ตฌ๊ฐ์ ์ ์๋ ๋ฌด์์ธ๊ฐ์?
- p-value๋ฅผ ๋ชจ๋ฅด๋ ์ฌ๋์๊ฒ ์ค๋ช ํ๋ค๋ฉด ์ด๋ป๊ฒ ์ค๋ช ํ์ค ๊ฑด๊ฐ์?
- R square์ ์๋ฏธ๋ ๋ฌด์์ธ๊ฐ์?
- ํ๊ท (mean)๊ณผ ์ค์๊ฐ(median)์ค์ ์ด๋ค ์ผ์ด์ค์์ ๋ญ๋ฅผ ์จ์ผํ ๊น์?
- ์ค์ฌ๊ทนํ์ ๋ฆฌ๋ ์ ์ ์ฉํ๊ฑธ๊น์?
- ์ํธ๋กํผ(entropy)์ ๋ํด ์ค๋ช ํด์ฃผ์ธ์. ๊ฐ๋ฅํ๋ฉด Information Gain๋์.
- ์ด๋จ ๋ ๋ชจ์์ ๋ฐฉ๋ฒ๋ก ์ ์ธ ์ ์๊ณ , ์ด๋จ ๋ ๋น๋ชจ์์ ๋ฐฉ๋ฒ๋ก ์ ์ธ ์ ์๋์?
- โlikelihoodโ์ โprobabilityโ์ ์ฐจ์ด๋ ๋ฌด์์ผ๊น์?
- ํต๊ณ์์ ์ฌ์ฉ๋๋ bootstrap์ ์๋ฏธ๋ ๋ฌด์์ธ๊ฐ์.
- ๋ชจ์๊ฐ ๋งค์ฐ ์ ์ (์์ญ๊ฐ ์ดํ) ์ผ์ด์ค์ ๊ฒฝ์ฐ ์ด๋ค ๋ฐฉ์์ผ๋ก ์์ธก ๋ชจ๋ธ์ ์๋ฆฝํ ์ ์์๊น์?
- ๋ฒ ์ด์ง์๊ณผ ํ๋ฆฌํํฐ์คํธ ๊ฐ์ ์ ์ฅ์ฐจ์ด๋ฅผ ์ค๋ช ํด์ฃผ์ค ์ ์๋์?
- ๊ฒ์ ๋ ฅ(statistical power)์ ๋ฌด์์ผ๊น์?
- missing value๊ฐ ์์ ๊ฒฝ์ฐ ์ฑ์์ผ ํ ๊น์? ๊ทธ ์ด์ ๋ ๋ฌด์์ธ๊ฐ์?
- ์์๋ผ์ด์ด์ ํ๋จํ๋ ๊ธฐ์ค์ ๋ฌด์์ธ๊ฐ์?
- ํ์ํ ํ๋ณธ์ ํฌ๊ธฐ๋ฅผ ์ด๋ป๊ฒ ๊ณ์ฐํฉ๋๊น?
- Bias๋ฅผ ํต์ ํ๋ ๋ฐฉ๋ฒ์ ๋ฌด์์ ๋๊น?
- ๋ก๊ทธ ํจ์๋ ์ด๋ค ๊ฒฝ์ฐ ์ ์ฉํฉ๋๊น? ์ฌ๋ก๋ฅผ ๋ค์ด ์ค๋ช ํด์ฃผ์ธ์.
- ๋ฒ ๋ฅด๋์ด ๋ถํฌ / ์ดํญ ๋ถํฌ / ์นดํ ๊ณ ๋ฆฌ ๋ถํฌ / ๋คํญ ๋ถํฌ / ๊ฐ์ฐ์์ ์ ๊ท ๋ถํฌ / t ๋ถํฌ / ์นด์ด์ ๊ณฑ ๋ถํฌ / F ๋ถํฌ / ๋ฒ ํ ๋ถํฌ / ๊ฐ๋ง ๋ถํฌ์ ๋ํด ์ค๋ช ํด์ฃผ์ธ์. ๊ทธ๋ฆฌ๊ณ ๋ถํฌ ๊ฐ์ ์ฐ๊ด์ฑ๋ ์ค๋ช ํด์ฃผ์ธ์.
- ์ถ์ฅ์ ์ํด ๋นํ๊ธฐ๋ฅผ ํ๋ ค๊ณ ํฉ๋๋ค. ๋น์ ์ ์ฐ์ฐ์ ๊ฐ์ ธ๊ฐ์ผ ํ๋์ง ์๊ณ ์ถ์ด ์ถ์ฅ์ง์ ์ฌ๋ ์น๊ตฌ 3๋ช ์๊ฒ ๋ฌด์์๋ก ์ ํ๋ฅผ ํ๊ณ ๋น๊ฐ ์ค๋ ๊ฒฝ์ฐ๋ฅผ ๋ ๋ฆฝ์ ์ผ๋ก ์ง๋ฌธํด์ฃผ์ธ์. ๊ฐ ์น๊ตฌ๋ 2/3๋ก ์ง์ค์ ๋งํ๊ณ 1/3์ผ๋ก ๊ฑฐ์ง์ ๋งํฉ๋๋ค. 3๋ช ์ ์น๊ตฌ๊ฐ ๋ชจ๋ โ๊ทธ๋ ์ต๋๋ค. ๋น๊ฐ ๋ด๋ฆฌ๊ณ ์์ต๋๋คโ๋ผ๊ณ ๋งํ์ต๋๋ค. ์ค์ ๋ก ๋น๊ฐ ๋ด๋ฆด ํ๋ฅ ์ ์ผ๋ง์ ๋๊น?
๐ค ๋จธ์ ๋ฌ๋
- ์๊ณ ์๋ metric์ ๋ํด ์ค๋ช ํด์ฃผ์ธ์. (ex. RMSE, MAE, recall, precision ...)
- ์ ๊ทํ๋ฅผ ์ ํด์ผํ ๊น์? ์ ๊ทํ์ ๋ฐฉ๋ฒ์ ๋ฌด์์ด ์๋์?
- Local Minima์ Global Minimum์ ๋ํด ์ค๋ช ํด์ฃผ์ธ์.
- ์ฐจ์์ ์ ์ฃผ์ ๋ํด ์ค๋ช ํด์ฃผ์ธ์.
- dimension reduction๊ธฐ๋ฒ์ผ๋ก ๋ณดํต ์ด๋ค ๊ฒ๋ค์ด ์๋์?
- PCA๋ ์ฐจ์ ์ถ์ ๊ธฐ๋ฒ์ด๋ฉด์, ๋ฐ์ดํฐ ์์ถ ๊ธฐ๋ฒ์ด๊ธฐ๋ ํ๊ณ , ๋ ธ์ด์ฆ ์ ๊ฑฐ๊ธฐ๋ฒ์ด๊ธฐ๋ ํฉ๋๋ค. ์ ๊ทธ๋ฐ์ง ์ค๋ช ํด์ฃผ์ค ์ ์๋์?
- LSA, LDA, SVD ๋ฑ์ ์ฝ์๋ค์ด ์ด๋ค ๋ป์ด๊ณ ์๋ก ์ด๋ค ๊ด๊ณ๋ฅผ ๊ฐ์ง๋์ง ์ค๋ช ํ ์ ์๋์?
- Markov Chain์ ๊ณ ๋ฑํ์์๊ฒ ์ค๋ช ํ๋ ค๋ฉด ์ด๋ค ๋ฐฉ์์ด ์ ์ผ ์ข์๊น์?
- ํ ์คํธ ๋๋ฏธ์์ ์ฃผ์ ๋ฅผ ์ถ์ถํด์ผ ํฉ๋๋ค. ์ด๋ค ๋ฐฉ์์ผ๋ก ์ ๊ทผํด ๋๊ฐ์๊ฒ ๋์?
- SVM์ ์ ๋ฐ๋๋ก ์ฐจ์์ ํ์ฅ์ํค๋ ๋ฐฉ์์ผ๋ก ๋์ํ ๊น์? SVM์ ์ ์ข์๊น์?
- ๋ค๋ฅธ ์ข์ ๋จธ์ ๋ฌ๋ ๋๋น, ์ค๋๋ ๊ธฐ๋ฒ์ธ ๋์ด๋ธ ๋ฒ ์ด์ฆ(naive bayes)์ ์ฅ์ ์ ์นํธํด๋ณด์ธ์.
- ํ๊ท / ๋ถ๋ฅ์ ์๋ง์ metric์ ๋ฌด์์ผ๊น?
- Association Rule์ Support, Confidence, Lift์ ๋ํด ์ค๋ช ํด์ฃผ์ธ์.
- ์ต์ ํ ๊ธฐ๋ฒ์ค Newtonโs Method์ Gradient Descent ๋ฐฉ๋ฒ์ ๋ํด ์๊ณ ์๋์?
- ๋จธ์ ๋ฌ๋(machine)์ ์ ๊ทผ๋ฐฉ๋ฒ๊ณผ ํต๊ณ(statistics)์ ์ ๊ทผ๋ฐฉ๋ฒ์ ๋๊ฐ์ ์ฐจ์ด์ ๋ํ ๊ฒฌํด๊ฐ ์๋์?
- ์ธ๊ณต์ ๊ฒฝ๋ง(deep learning์ด์ ์ ์ ํต์ ์ธ)์ด ๊ฐ์ง๋ ์ผ๋ฐ์ ์ธ ๋ฌธ์ ์ ์ ๋ฌด์์ผ๊น์?
- ์ง๊ธ ๋์ค๊ณ ์๋ deep learning ๊ณ์ด์ ํ์ ์ ๊ทผ๊ฐ์ ๋ฌด์์ด๋ผ๊ณ ์๊ฐํ์๋์?
- ROC ์ปค๋ธ์ ๋ํด ์ค๋ช ํด์ฃผ์ค ์ ์์ผ์ ๊ฐ์?
- ์ฌ๋ฌ๋ถ์ด ์๋ฒ๋ฅผ 100๋ ๊ฐ์ง๊ณ ์์ต๋๋ค. ์ด๋ ์ธ๊ณต์ ๊ฒฝ๋ง๋ณด๋ค Random Forest๋ฅผ ์จ์ผํ๋ ์ด์ ๋ ๋ญ๊น์?
- K-means์ ๋ํ์ ์๋ฏธ๋ก ์ ๋จ์ ์ ๋ฌด์์ธ๊ฐ์? (๊ณ์ฐ๋ ๋ง๋ค๋๊ฒ ๋ง๊ณ )
- L1, L2 ์ ๊ทํ์ ๋ํด ์ค๋ช ํด์ฃผ์ธ์.
- Cross Validation์ ๋ฌด์์ด๊ณ ์ด๋ป๊ฒ ํด์ผํ๋์?
- XGBoost์ ์์๋์? ์ ์ด ๋ชจ๋ธ์ด ์บ๊ธ์์ ์ ๋ช ํ ๊น์?
- ์์๋ธ ๋ฐฉ๋ฒ์ ์ด๋ค ๊ฒ๋ค์ด ์๋์?
- feature vector๋ ๋ฌด์์ผ๊น์?
- ์ข์ ๋ชจ๋ธ์ ์ ์๋ ๋ฌด์์ผ๊น์?
- 50๊ฐ์ ์์ ์์ฌ๊ฒฐ์ ๋๋ฌด๋ ํฐ ์์ฌ๊ฒฐ์ ๋๋ฌด๋ณด๋ค ๊ด์ฐฎ์๊น์? ์ ๊ทธ๋ ๊ฒ ์๊ฐํ๋์?
- ์คํธ ํํฐ์ ๋ก์ง์คํฑ ๋ฆฌ๊ทธ๋ ์ ์ ๋ง์ด ์ฌ์ฉํ๋ ์ด์ ๋ ๋ฌด์์ผ๊น์?
- OLS(ordinary least squre) regression์ ๊ณต์์ ๋ฌด์์ธ๊ฐ์?
๐ง ๋ฅ๋ฌ๋
- ๋ฅ๋ฌ๋์ ๋ฌด์์ธ๊ฐ์? ๋ฅ๋ฌ๋๊ณผ ๋จธ์ ๋ฌ๋์ ์ฐจ์ด๋?
- Cost Function๊ณผ Activation Function์ ๋ฌด์์ธ๊ฐ์?
- Tensorflow, PyTorch ํน์ง๊ณผ ์ฐจ์ด๊ฐ ๋ญ๊น์?
- Data Normalization์ ๋ฌด์์ด๊ณ ์ ํ์ํ๊ฐ์?
- ์๊ณ ์๋ Activation Function์ ๋ํด ์๋ ค์ฃผ์ธ์. (Sigmoid, ReLU, LeakyReLU, Tanh ๋ฑ)
- ์ค๋ฒํผํ ์ผ ๊ฒฝ์ฐ ์ด๋ป๊ฒ ๋์ฒํด์ผ ํ ๊น์?
- ํ์ดํผ ํ๋ผ๋ฏธํฐ๋ ๋ฌด์์ธ๊ฐ์?
- Weight Initialization ๋ฐฉ๋ฒ์ ๋ํด ๋งํด์ฃผ์ธ์. ๊ทธ๋ฆฌ๊ณ ๋ฌด์์ ๋ง์ด ์ฌ์ฉํ๋์?
- ๋ณผ์ธ ๋ง ๋จธ์ ์ ๋ฌด์์ธ๊ฐ์?
- TF, PyTorch ๋ฑ์ ์ฌ์ฉํ ๋ ๋๋ฒ๊น ๋ ธํ์ฐ๋?
- ๋ด๋ด๋ท์ ๊ฐ์ฅ ํฐ ๋จ์ ์ ๋ฌด์์ธ๊ฐ? ์ด๋ฅผ ์ํด ๋์จ One-Shot Learning์ ๋ฌด์์ธ๊ฐ?
- ์์ฆ Sigmoid ๋ณด๋ค ReLU๋ฅผ ๋ง์ด ์ฐ๋๋ฐ ๊ทธ ์ด์ ๋?
- Non-Linearity๋ผ๋ ๋ง์ ์๋ฏธ์ ๊ทธ ํ์์ฑ์?
- ReLU๋ก ์ด๋ป๊ฒ ๊ณก์ ํจ์๋ฅผ ๊ทผ์ฌํ๋?
- ReLU์ ๋ฌธ์ ์ ์?
- Bias๋ ์ ์๋๊ฑธ๊น?
- Gradient Descent์ ๋ํด์ ์ฝ๊ฒ ์ค๋ช
ํ๋ค๋ฉด?
- ์ ๊ผญ Gradient๋ฅผ ์จ์ผ ํ ๊น? ๊ทธ ๊ทธ๋ํ์์ ๊ฐ๋ก์ถ๊ณผ ์ธ๋ก์ถ ๊ฐ๊ฐ์ ๋ฌด์์ธ๊ฐ? ์ค์ ์ํฉ์์๋ ๊ทธ ๊ทธ๋ํ๊ฐ ์ด๋ป๊ฒ ๊ทธ๋ ค์ง๊น?
- GD ์ค์ ๋๋๋ก Loss๊ฐ ์ฆ๊ฐํ๋ ์ด์ ๋?
- Back Propagation์ ๋ํด์ ์ฝ๊ฒ ์ค๋ช ํ๋ค๋ฉด?
- Local Minima ๋ฌธ์ ์๋ ๋ถ๊ตฌํ๊ณ ๋ฅ๋ฌ๋์ด ์ ๋๋ ์ด์ ๋?
- GD๊ฐ Local Minima ๋ฌธ์ ๋ฅผ ํผํ๋ ๋ฐฉ๋ฒ์?
- ์ฐพ์ ํด๊ฐ Global Minimum์ธ์ง ์๋์ง ์ ์ ์๋ ๋ฐฉ๋ฒ์?
- Training ์ธํธ์ Test ์ธํธ๋ฅผ ๋ถ๋ฆฌํ๋ ์ด์ ๋?
- Validation ์ธํธ๊ฐ ๋ฐ๋ก ์๋ ์ด์ ๋?
- Test ์ธํธ๊ฐ ์ค์ผ๋์๋ค๋ ๋ง์ ๋ป์?
- Regularization์ด๋ ๋ฌด์์ธ๊ฐ?
- Batch Normalization์ ํจ๊ณผ๋?
- Dropout์ ํจ๊ณผ๋?
- BN ์ ์ฉํด์ ํ์ต ์ดํ ์ค์ ์ฌ์ฉ์์ ์ฃผ์ํ ์ ์? ์ฝ๋๋ก๋?
- GAN์์ Generator ์ชฝ์๋ BN์ ์ ์ฉํด๋ ๋ ๊น?
- SGD, RMSprop, Adam์ ๋ํด์ ์๋๋๋ก ์ค๋ช
ํ๋ค๋ฉด?
- SGD์์ Stochastic์ ์๋ฏธ๋?
- ๋ฏธ๋๋ฐฐ์น๋ฅผ ์๊ฒ ํ ๋์ ์ฅ๋จ์ ์?
- ๋ชจ๋ฉํ ์ ์์์ ์ ์ด ๋ณธ๋ค๋ฉด?
- ๊ฐ๋จํ MNIST ๋ถ๋ฅ๊ธฐ๋ฅผ MLP+CPU ๋ฒ์ ์ผ๋ก numpy๋ก ๋ง๋ ๋ค๋ฉด ๋ช์ค์ผ๊น?
- ์ด๋ ์ ๋ ๋์๊ฐ๋ ๋ ์์ ์์ฑํ๊ธฐ๊น์ง ๋ช์๊ฐ ์ ๋ ๊ฑธ๋ฆด๊น?
- Back Propagation์ ๋ช์ค์ธ๊ฐ?
- CNN์ผ๋ก ๋ฐ๊พผ๋ค๋ฉด ์ผ๋ง๋ ์ถ๊ฐ๋ ๊น?
- ๊ฐ๋จํ MNIST ๋ถ๋ฅ๊ธฐ๋ฅผ TF, PyTorch ๋ฑ์ผ๋ก ์์ฑํ๋๋ฐ ๋ช์๊ฐ์ด ํ์ํ๊ฐ?
- CNN์ด ์๋ MLP๋ก ํด๋ ์ ๋ ๊น?
- ๋ง์ง๋ง ๋ ์ด์ด ๋ถ๋ถ์ ๋ํด์ ์ค๋ช ํ๋ค๋ฉด?
- ํ์ต์ BCE loss๋ก ํ๋ ์ํฉ์ MSE loss๋ก ๋ณด๊ณ ์ถ๋ค๋ฉด?
- ๋ฅ๋ฌ๋ํ ๋ GPU๋ฅผ ์ฐ๋ฉด ์ข์ ์ด์ ๋?
- GPU๋ฅผ ๋๊ฐ ๋ค ์ฐ๊ณ ์ถ๋ค. ๋ฐฉ๋ฒ์?
- ํ์ต์ ํ์ํ GPU ๋ฉ๋ชจ๋ฆฌ๋ ์ด๋ป๊ฒ ๊ณ์ฐํ๋๊ฐ?
๐ ํ์ด์ฌ
- What is the difference between list and tuples in Python?
- What are the key features of Python?
- What type of language is python? Programming or scripting?
- Python an interpreted language. Explain.
- What is pep 8?
- How is memory managed in Python?
- What is namespace in Python?
- What is PYTHONPATH?
- What are python modules? Name some commonly used built-in modules in Python?
- What are local variables and global variables in Python?
- Is python case sensitive?
- What is type conversion in Python?
- How to install Python on Windows and set path variable?
- Is indentation required in python?
- What is the difference between Python Arrays and lists?
- What are functions in Python?
- What is
__init__
? - What is a lambda function?
- What is self in Python?
- How does break, continue and pass work?
- What does
[::-1]
do? - How can you randomize the items of a list in place in Python?
- Whatโs the difference between iterator and iterable?
- How can you generate random numbers in Python?
- What is the difference between range & xrange?
- How do you write comments in python?
- What is pickling and unpickling?
- What are the generators in python?
- How will you capitalize the first letter of string?
- How will you convert a string to all lowercase?
- How to comment multiple lines in python?
- What are docstrings in Python?
- What is the purpose of is, not and in operators?
- What is the usage of help() and dir() function in Python?
- Whenever Python exits, why isnโt all the memory de-allocated?
- What is a dictionary in Python?
- How can the ternary operators be used in python?
- What does this mean:
*args
,**kwargs
? And why would we use it? - What does len() do?
- Explain split(), sub(), subn() methods of โreโ module in Python.
- What are negative indexes and why are they used?
- What are Python packages?
- How can files be deleted in Python?
- What are the built-in types of python?
- What advantages do NumPy arrays offer over (nested) Python lists?
- How to add values to a python array?
- How to remove values to a python array?
- Does Python have OOps concepts?
- What is the difference between deep and shallow copy?
- How is Multithreading achieved in Python?
- What is the process of compilation and linking in python?
- What are Python libraries? Name a few of them.
- What is split used for?
- How to import modules in python?
- Explain Inheritance in Python with an example.
- How are classes created in Python?
- What is monkey patching in Python?
- Does python support multiple inheritance?
- What is Polymorphism in Python?
- Define encapsulation in Python?
- How do you do data abstraction in Python?
- Does python make use of access specifiers?
- How to create an empty class in Python?
- What does an object() do?
- What is map function in Python?
- Is python numpy better than lists?
- What is GIL in Python language?
- What makes the CPython different from Python?
- What are Decorators in Python?
- What is object interning?
- What is @classmethod, @staticmethod, @property?
๐ ๋คํธ์ํฌ
- TCP/IP์ ๊ฐ ๊ณ์ธต์ ์ค๋ช ํด์ฃผ์ธ์.
- OSI 7๊ณ์ธต์ TCP/IP ๊ณ์ธต์ ์ฐจ์ด๋ฅผ ์ค๋ช ํด์ฃผ์ธ์.
- Frame, Packet, Segment, Datagram์ ๋น๊ตํด์ฃผ์ธ์.
- TCP์ UDP์ ์ฐจ์ด๋ฅผ ์ค๋ช ํด์ฃผ์ธ์.
- TCP์ UDP์ ํค๋๋ฅผ ๋น๊ตํด์ฃผ์ธ์.
- TCP์ 3-way-handshake์ 4-way-handshake๋ฅผ ๋น๊ต ์ค๋ช ํด์ฃผ์ธ์.
- TCP์ ์ฐ๊ฒฐ ์ค์ ๊ณผ์ (3๋จ๊ณ)๊ณผ ์ฐ๊ฒฐ ์ข ๋ฃ ๊ณผ์ (4๋จ๊ณ)์ด ๋จ๊ณ๊ฐ ์ฐจ์ด๋๋ ์ด์ ๊ฐ ๋ฌด์์ธ๊ฐ์?
- ๋ง์ฝ Server์์ FIN ํ๋๊ทธ๋ฅผ ์ ์กํ๊ธฐ ์ ์ ์ ์กํ ํจํท์ด Routing ์ง์ฐ์ด๋ ํจํท ์ ์ค๋ก ์ธํ ์ฌ์ ์ก ๋ฑ์ผ๋ก ์ธํด FIN ํจํท๋ณด๋ค ๋ฆ๊ฒ ๋์ฐฉํ๋ ์ํฉ์ด ๋ฐ์ํ๋ฉด ์ด๋ป๊ฒ ๋ ๊น์?
- ์ด๊ธฐ Sequence Number์ธ ISN์ 0๋ถํฐ ์์ํ์ง ์๊ณ ๋์๋ฅผ ์์ฑํด์ ์ค์ ํ๋ ์ด์ ๊ฐ ๋ฌด์์ธ๊ฐ์?
- HTTP์ HTTPS์ ๋ํด์ ์ค๋ช ํ๊ณ ์ฐจ์ด์ ์ ๋ํด ์ค๋ช ํด์ฃผ์ธ์.
- HTTP ์์ฒญ/์๋ต ํค๋์ ๊ตฌ์กฐ๋ฅผ ์ค๋ช ํด์ฃผ์ธ์.
- HTTP์ HTTPS ๋์ ๊ณผ์ ์ ๋น๊ตํด์ฃผ์ธ์.
- CORS๊ฐ ๋ฌด์์ธ๊ฐ์?
- HTTP GET๊ณผ POST ๋ฉ์๋๋ฅผ ๋น๊ต/์ค๋ช ํด์ฃผ์ธ์.
- ์ฟ ํค(Cookie)์ ์ธ์ (Session)์ ์ค๋ช ํด์ฃผ์ธ์.
- DNS๊ฐ ๋ฌด์์ธ๊ฐ์?
- REST์ RESTful์ ๊ฐ๋ ์ ์ค๋ช ํ๊ณ ์ฐจ์ด๋ฅผ ๋งํด์ฃผ์ธ์.
- ์์ผ(Socket)์ด ๋ฌด์์ธ๊ฐ์? ์์ ์๋ ์ธ์ด๋ก ๊ฐ๋จํ ์์ผ ์์ฑ ์์๋ฅผ ๋ณด์ฌ์ฃผ์ธ์.
- Socket.io์ WebSocket์ ์ฐจ์ด๋ฅผ ์ค๋ช ํด์ฃผ์ธ์.
- IPv4์ IPv6 ์ฐจ์ด๋ฅผ ์ค๋ช ํด์ฃผ์ธ์.
- MAC Address๊ฐ ๋ฌด์์ธ๊ฐ์?
- ๋ผ์ฐํฐ์ ์ค์์น, ํ๋ธ์ ์ฐจ์ด๋ฅผ ์ค๋ช ํด์ฃผ์ธ์.
- SMTP๊ฐ ๋ฌด์์ธ๊ฐ์?
- ๋
ธํธ๋ถ์ผ๋ก
www.google.com
์ ์ ์์ ํ์ต๋๋ค. ์์ฒญ์ ๋ณด๋ด๊ณ ๋ฐ๊ธฐ๊น์ง์ ๊ณผ์ ์ ์์ธํ ์ค๋ช ํด์ฃผ์ธ์. - ์ฌ๋ฌ ๋คํธ์ํฌ topology์ ๋ํด ๊ฐ๋จํ ์๊ฐํด์ฃผ์ธ์.
- subnet mask์ ๋ํด์ ์ค๋ช ํด์ฃผ์ธ์.
- data encapsulation์ด ๋ฌด์์ธ๊ฐ์?
- DHCP๋ฅผ ์ค๋ช ํด์ฃผ์ธ์.
- routing protocol์ ๋ช ๊ฐ์ง ์ค๋ช ํด์ฃผ์ธ์. (ex. link state, distance vector)
- ์ด๋๋ท(ethernet)์ด ๋ฌด์์ธ๊ฐ์?
- client์ server์ ์ฐจ์ด์ ์ ์ค๋ช ํด์ฃผ์ธ์.
- delay, timing(jitter), throughput ์ฐจ์ด๋ฅผ ์ค๋ช ํด์ฃผ์ธ์.
๐ฅ๏ธ ์ด์์ฒด์
- ํ๋ก์ธ์ค์ ์ค๋ ๋์ ์ฐจ์ด(Process vs Thread)๋ฅผ ์๋ ค์ฃผ์ธ์.
- ๋ฉํฐ ํ๋ก์ธ์ค ๋์ ๋ฉํฐ ์ค๋ ๋๋ฅผ ์ฌ์ฉํ๋ ์ด์ ๋ฅผ ์ค๋ช ํด์ฃผ์ธ์.
- ์บ์์ ์ง์ญ์ฑ์ ๋ํด ์ค๋ช ํด์ฃผ์ธ์.
- Thread-safe์ ๋ํด ์ค๋ช ํด์ฃผ์ธ์. (hint: critical section)
- ๋ฎคํ ์ค์ ์ธ๋งํฌ์ด์ ์ฐจ์ด๋ฅผ ์ค๋ช ํด์ฃผ์ธ์.
- ์ค์ผ์ค๋ฌ๊ฐ ๋ฌด์์ด๊ณ , ๋จ๊ธฐ/์ค๊ธฐ/์ฅ๊ธฐ๋ก ๋๋๋ ๊ธฐ์ค์ ๋ํด ์ค๋ช ํด์ฃผ์ธ์.
- CPU ์ค์ผ์ค๋ฌ์ธ FCFS, SJF, SRTF, Priority Scheduling, RR์ ๋ํด ๊ฐ๋ตํ ์ค๋ช ํด์ฃผ์ธ์.
- ๋๊ธฐ์ ๋น๋๊ธฐ์ ์ฐจ์ด๋ฅผ ์ค๋ช ํด์ฃผ์ธ์.
- ๋ฉ๋ชจ๋ฆฌ ๊ด๋ฆฌ ์ ๋ต์๋ ๋ฌด์์ด ์๋์ง ๊ฐ๋ตํ ์ค๋ช ํด์ฃผ์ธ์.
- ๊ฐ์ ๋ฉ๋ชจ๋ฆฌ์ ๋ํด ์ค๋ช ํด์ฃผ์ธ์.
- ๊ต์ฐฉ์ํ(๋ฐ๋๋ฝ, Deadlock)์ ๊ฐ๋ ๊ณผ ์กฐ๊ฑด์ ์ค๋ช ํด์ฃผ์ธ์.
- ์ฌ์ฉ์ ์์ค ์ค๋ ๋์ ์ปค๋ ์์ค ์ค๋ ๋์ ์ฐจ์ด๋ฅผ ์ค๋ช ํด์ฃผ์ธ์.
- ์ธ๋ถ ๋จํธํ์ ๋ด๋ถ ๋จํธํ์ ๋ํด ์ค๋ช ํด์ฃผ์ธ์.
- Context Switching์ด ๋ฌด์์ธ์ง ์ค๋ช ํ๊ณ ๊ณผ์ ์ ๋์ดํด์ฃผ์ธ์.
- Swapping์ ๋ํด ์ค๋ช ํด์ฃผ์ธ์.
๐ ์๋ฃ๊ตฌ์กฐ
- linked list
- single linked list
- double linked list
- circular linked list
- hash table
- stack
- queue
- circular queue
- graph
- tree
- binary tree
- full binary tree
- complete binary tree
- bst(binary search tree)
- heap(binary heap)
- min heap
- max heap
- red-black tree
- b+ tree
๐ป ์๊ณ ๋ฆฌ์ฆ
- ์๊ฐ, ๊ณต๊ฐ ๋ณต์ก๋
- Sort Algorithm
- Bubble Sort
- Selection Sort
- Insertion Sort
- Merge Sort
- Heap Sort
- Quick Sort
- Counting Sort
- Radix Sort
- Divide and Conquer
- Dynamic Programming
- Greedy Algorithm
- Graph
- Graph Traversal: BFS, DFS
- Shortest Path
- Dijkstra
- Floyd-Warshall
- Bellman-Ford
- Minimum Spanning Tree
- Prim
- Kruskal
- Union-find
- Topological sort
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The VisionCraft API is a free API for using over 100 different AI models. From images to sound.

kaito
Kaito is an operator that automates the AI/ML inference model deployment in a Kubernetes cluster. It manages large model files using container images, avoids tuning deployment parameters to fit GPU hardware by providing preset configurations, auto-provisions GPU nodes based on model requirements, and hosts large model images in the public Microsoft Container Registry (MCR) if the license allows. Using Kaito, the workflow of onboarding large AI inference models in Kubernetes is largely simplified.

PyRIT
PyRIT is an open access automation framework designed to empower security professionals and ML engineers to red team foundation models and their applications. It automates AI Red Teaming tasks to allow operators to focus on more complicated and time-consuming tasks and can also identify security harms such as misuse (e.g., malware generation, jailbreaking), and privacy harms (e.g., identity theft). The goal is to allow researchers to have a baseline of how well their model and entire inference pipeline is doing against different harm categories and to be able to compare that baseline to future iterations of their model. This allows them to have empirical data on how well their model is doing today, and detect any degradation of performance based on future improvements.

tabby
Tabby is a self-hosted AI coding assistant, offering an open-source and on-premises alternative to GitHub Copilot. It boasts several key features: * Self-contained, with no need for a DBMS or cloud service. * OpenAPI interface, easy to integrate with existing infrastructure (e.g Cloud IDE). * Supports consumer-grade GPUs.

spear
SPEAR (Simulator for Photorealistic Embodied AI Research) is a powerful tool for training embodied agents. It features 300 unique virtual indoor environments with 2,566 unique rooms and 17,234 unique objects that can be manipulated individually. Each environment is designed by a professional artist and features detailed geometry, photorealistic materials, and a unique floor plan and object layout. SPEAR is implemented as Unreal Engine assets and provides an OpenAI Gym interface for interacting with the environments via Python.

Magick
Magick is a groundbreaking visual AIDE (Artificial Intelligence Development Environment) for no-code data pipelines and multimodal agents. Magick can connect to other services and comes with nodes and templates well-suited for intelligent agents, chatbots, complex reasoning systems and realistic characters.