Data Science Trivia — 270 Data Science Interview Questions

Machine Learning

What is supervised learning?

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What is supervised learning, and what are some examples?
What is a ROC curve?
What are the types of kernels in SVM?
What is pruning in a decision tree?
What is ensemble learning?
Can you use k-fold cross-validation on time series data?
What is the process of adding tuning parameters to a model to induce smoothness in order to prevent overfitting?
What is count encoding?
What is one hot encoding?
What is target encoding?
What is data leakage?
Imagine you are applying a decision-tree-based model to a numerical dataset and you run an experiment where you scale the features. Which model would give better results, the original or the scaled one?
What is the difference between L1 (Lasso) and L2 (Ridge) regularization?
What is a confusion matrix?
Should a confusion matrix be used for supervised or unsupervised learning problems?
What is boosting when referring to machine learning algorithms?
What is bucketing in machine learning?
How many centroids would a k-means algorithm with k = 3 find?
What is the difference between a dense and a sparse feature?
What is downsampling in the context of class-imbalanced dataset?
What is early stopping?
Is logistic regression useful for estimating continuous variables?
What is it called when a model fits the training data too closely?
How does a random forest work?
What does "random" mean in the term random forest?
What is underfitting in machine learning?
What is the difference between precision and recall?
What is the F1 score?
What is Elastic net regularization?
What is unsupervised learning, and what are some examples?
Does feature selection tend to increase overfitting?
Mean Squared Error, Root Mean Squared Error, Mean Absolute Error, R-squared are metrics for evaluation which type of models?
Accuracy, Precision, Recall, F1 score, ... are metrics to evaluate which types of models?
Why is naive Bayes 'naive'?
Is KNN a clustering algorithm?
How is random forest different from gradient boosting algorithm?
Is it possible to design a linear regression algorithm using a neural network?
Which regularization would you use if your model is underfitting?
Is standardization of features required before training a logistic regression?
What is the difference between online and batch learning?
What is bagging (bootstrap aggregating) in machine learning?
What is the difference between boosting and bagging?
High bias can cause an algorithm to underfit? True or False?
High variance can cause an algorithm to overfit. True or False?
What is variance in the context of machine learning?
What is the bias-variance tradeoff?
What happens to our linear regression if we have columns x, y and z, and z is the sum of x and y?
Is logistic regression a linear model? Why?
What is TF-IDF?
What is overfitting?
What is the precision-recall curve?
Can we use L1 regularization for feature selection?
What is bag of words?
What is clustering?
What is a time series?
When building your vocabulary, is it OK to use the tokens from the entire dataset to shrink your out-of-vocabulary list?
What is the main data structure used in causal modelling?
What is the difference between sensitivity and specificity?
What is a calibration curve in machine learning?
What is the Brier score?
What is log loss (cross-entropy loss)?
What is hinge loss?
What is Huber loss?
What is pinball loss (quantile loss)?
What is MAPE?
What is WAPE?
What is NDCG?
What is Cohen's kappa?
What is the Matthews correlation coefficient?
What is the generalization of the median?
What is the difference between "long" and "wide" format data?
What is cluster sampling?
What is a Type I and a Type II error?
What two parameters define a normal distribution?
In a normal distribution which parameter indicates where the bell is centered?
In a normal distribution, which parameter indicates how wide the bell is?
What is the difference between a nominal and an ordinal feature?
What does high and low cardinality mean?
What is R-squared?
What is a residual?
What is dimensionality reduction?
What is the difference between a false positive and a false negative?
Values distant from most other values are?
Precision is the ratio between which two quantities?
What does stationarity in a dataset mean?
What is the central limit theorem?
What are the common dimensionality reduction techniques?
What measures how far a set of numbers is spread out from their average?
What values could you infer from a boxplot?
Is rotation necessary in PCA?
What is the difference between covariance and correlation?
Is it possible to capture the correlation between a continuous and a categorical variable?
If pearson correlation is 0 between two variables can we assume that there isn't any relation between them?
Does mean imputation take feature correlation into account?
How do you handle missing values in a dataset?
What is the Law of Large Numbers?
What is Survivorship bias?
What is a confounding variable?
How is the interquartile range calculated?
What is autocorrelation?
What is MSE?
What is RMSE?
How can we select K for K-means?
What is precision and recall at k?
What is KL divergence?
What is the Jensen-Shannon divergence?
What is the Kolmogorov-Smirnov test?
What is a p-value?
What is a confidence interval?
What is heteroscedasticity?
What is the Poisson distribution?
What is the binomial distribution?
Why doesn't correlation imply causation?
What is Simpson's paradox?
What do skewness and kurtosis measure?
What is selection bias?
What is a probability density function?
What is maximum likelihood estimation?
What is statistical power?
What is a t-test?
What is the Bonferroni correction?
What is multicollinearity?
Bayesian vs frequentist statistics: what is the difference?
What is adjusted R-squared?
What is a chi-square test?
What is ANOVA?
What is the Mann-Whitney U test?
What is the Wilcoxon signed-rank test?
What is Fisher's exact test?
What is a z-test?
What is statistical significance?
What is conditional probability?
What is the law of total probability?
What is Bayes' theorem?
What is bootstrapping in statistics?
What is the Benjamini-Hochberg procedure?
What is p-hacking?
What is CUPED in A/B testing?
What is difference-in-differences?
What is an instrumental variable?
What is regression discontinuity design?
Who is known as the creator of the GAN architecture?
LeCun, Cortes, and Burges compiled a very famous dataset with 60,000 training images of handwritten digits. What is the name of this dataset?
The least squares method was first published by which famous French mathematician?
In 1812 Laplace published "Théorie analytique des probabilités", in which he stated a famous theorem. Which theorem was it?
Andrey Markov first described the techniques he used to analyse a poem, and these techniques later became known as Markov chains. True or false?
In 1951 Marvin Minsky and Dean Edmonds conceived what is known as the first neural network machine. What is its name?
Who invented the perceptron?
What did Seppo Linnainmaa's 1970 master's thesis introduce?
In which decade did Tin Kam Ho publish a paper describing random decision forests?
What did Corinna Cortes and Vladimir Vapnik publish in 1995?
What was the name of the first computer to beat a reigning world chess champion?
Who invented the LSTM architecture in deep learning?
When was Torch released?
Who envisioned and created the ImageNet dataset?
What is the name of the computer program that first beat a professional Go player without a handicap?
Which computer scientists received the Turing Award for their contributions to deep learning?
When was the nearest neighbor algorithm created?
When was the Turing Test created?
Who coined the term deep learning in 2006?
When was TensorFlow launched?
Who is the creator of Keras?
PyTorch was primarily developed by which company?
Was the R language designed by statisticians?
Who are credited with the creation of R?
What did Christopher Watkins introduce in his 1989 PhD thesis?
Who is known as the founding father of convolutional nets?
Who are known as the "Godfathers of AI"?
Was Bayes's Theorem published after his death?
What object did Yann LeCun use in his NIPS 2016 keynote to describe the 'layers' of intelligence?
What defunct deep learning library was started in 2007 by the Montreal lab now known as Mila?
What language was the original AlexNet (ILSVRC 2012) written in?
What was the final score of the 2016 match between Google DeepMind’s AlphaGo and Go grandmaster Lee Sedol?
Whose notable students include Ruslan Salakhutdinov, Ilya Sutskever and Yann LeCun?
What was the output dimension of the embedding layers and sub-layers of the original Transformer (Attention Is All You Need, 2017)?
In 2016 Google’s AlphaGo won four out of five games of Go against Lee Sedol, a professional Go player from South Korea (9 dan rank). What hardware powered the AlphaGo software to victory?
What is the difference between batch, mini-batch and stochastic gradient descent?
What are some popular deep learning frameworks?
What is AdaGrad?
What are the two actors in a convolution operation?
How does dropout regularization work in deep learning?
What is an epoch?
What is a batch?
Which are the two components of a Generative Adversarial Network?
What is the function of the generator in a GAN?
What is the function of the discriminator in a GAN?
What does gradient descent try to minimize?
Which activation function outputs max(0, x)?
What does the softmax function do?
What is the learning rate, and what if it's too high or too low?
What does LSTM mean?
What type of layer typically has a cell, an input gate, an output gate and a forget gate?
Sigmoid squishes values to be between?
What does GRU mean?
Tanh function squishes values to be between?
Type of layer that typically has a reset gate and an update gate?
What if we set all the weights of a neural network to 0?
Why do we need activation functions?
What are the problems with sigmoid as an activation function?
What is ReLU? How is it better than sigmoid?
What’s pooling in CNN? Why do we need it?
Why did the embedding layers and all sub-layers of the original Transformer (Attention Is All You Need, 2017) need the same output dimension?
Is one-hot encoding good for high-cardinality categorical features?
Does adding hidden layers increase a neural network's capacity?
Should you initialize neural network weights to zero?
Does a higher dropout rate increase a neural network's capacity?
Should you use SGD when the dataset doesn't fit in memory?
Is batch normalization a backpropagation technique?
Is gradient descent used to reduce overfitting?
Does precision measure a model's ability to find only relevant samples?
Does recall measure a model's ability to find all relevant samples?
Does softmax return the input if it's positive and zero otherwise?
Is an activation function's main role to decide whether a neuron fires?
Does ReLU output values that sum to one?
Does an autoencoder learn by using its inputs as the target?
Are convolutional neural networks translation invariant?
Are autoencoders a supervised learning method?
Is giving each input exactly one class called multi-class classification?
Does dropout speed up neural network training?
Can a neural network with a single layer approximate any function?
Does leaky ReLU let small negative values pass through?
Is a categorical feature one that takes a limited set of values?
What is intersection over union (IoU)?
What is focal loss?
What is self-attention?
What is multi-head attention?
What is positional encoding in transformers?
What is beam search?
What is contrastive learning?
What is knowledge distillation?
What do temperature, top-p and top-k control?
When do you use RAG instead of fine-tuning?
What are the tradeoffs in RAG chunk size?
Why combine embeddings with keyword search?
How do you evaluate a RAG system?
What actually reduces hallucinations?
Why is a bigger context window not always the answer?
Prompt, system prompt, tools: what does each do?
Why did transformers replace RNNs for most NLP tasks?
How does BERT differ from a GPT-style model?
When should you build a workflow instead of an agent?
What is an embedding?
When do few-shot examples beat zero-shot prompting?
What does RLHF change about a base model?
How does byte pair encoding tokenization work?
What is the KV cache, and why does it cap concurrency?
What is DPO (direct preference optimization)?
What is LoRA (low-rank adaptation) fine-tuning?
What is speculative decoding?
What is LLM-as-a-judge?
What is chain-of-thought prompting?
What are the Chinchilla scaling laws?
What is a mixture of experts (MoE) model?
What is FlashAttention?
What is grouped-query attention?
What are BLEU and ROUGE scores?
What is data drift?
What is concept drift?
What is model drift?
What is training-serving skew?
What is the Population Stability Index (PSI)?
How do you monitor a model before its labels arrive?
What is the difference between batch and online inference?
What is a feature store?
What is shadow deployment?
What is a canary deployment?
What is the cold start problem?
What is a two-tower model?
How do you decide when to retrain a model?
Why can a model that wins offline lose in an A/B test?

Data Science Trivia — 270 data science interview questions and answers

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