Role-specific and topic-specific questions with answers
Easy, medium, and hard questions that cover most topics in machine learning and data science interviews. Solutions that deep dive into explaining complicated concepts, with necessary references and simulations when needed.
Check out our guides if you are interested in company-specific and role-specific interview prep!
| Problem | Role | Area | Topics | Difficulty | Company (login required) | Status |
|---|---|---|---|---|---|---|
| Sample Ratio Mismatch | DS PDS AS | A/B Testing | Sample Ratio Mismatch | Medium | Duolingo Snap Uber | |
| Questions worksheet (recruiter-screen) | DS AS MLE PDS | Application process | Discussion prep, Application prep | Medium | Meta Robinhood Tik Tok | |
| Questions worksheet (behavioral) | DS AS MLE PDS | Behavioral | Discussion prep | Hard | Chan-Zuckemberk Discord Uber | |
| Minimum remove to make valid parentheses (Leetcode 1249) | DS AS MLE | Data Structures and Algorithms | Stack | Medium | Amazon | |
| Remove duplicates in place (Leetcode 26) | DS AS MLE | Data Structures and Algorithms | Array, Two pointers | Easy | ||
| Bias-variance equation | DS AS MLE | Machine Learning | Bias-variance tradeoff, Formula derivation | Easy | Fidelity Google Salesforce Workiva | |
| Linear regression with gradient descent | DS AS MLE | Machine Learning Coding | Gradient descent, Linear regression | Medium | Apple Hinge Uber | |
| Gradient descent vs. stochastic gradient descent | DS AS MLE | Machine Learning | Gradient descent, Stochastic gradient descent, Minibatch | Medium | Paypal | |
| Imbalanced dataset | PDS DS AS MLE | Machine Learning | Class imbalance | Medium | Discord Paypal Salesforce | |
| Sudden drop in user engagement | DS PDS | Metrics | Problem solving, Root-cause analysis | Medium | Snap | |
| Documents edited by AI | DS PDS AS | Probability theory | Bayes rule, Conditional probability | Easy | Indeed Snap Thumbtack Warner Bros | |
| Trailing by two: should we go for two or three? | DS PDS AS | Probability theory | Independence, Decision making | Easy | Microsoft | |
| Artist maxranks | DS AS PDS | SQL | Join | Easy | Paypal | |
| Two-sample t-test | DS PDS AS | Statistics | Hypothesis testing | Easy | ||
| Choose a project | DS AS MLE PDS | Technical deep dive | Discussion prep | Hard | ||
| ( Login required ) Normality assumption | DS PDS AS | A/B Testing | Normality | Medium | ||
| ( Login required ) Reducing variance in AB testing | DS PDS AS | A/B Testing | Variance | Medium | Apple | |
| ( Login required ) Simpson’s paradox | DS PDS AS | A/B Testing | Simpson’s paradox | Medium | Fidelity Snap | |
| ( Login required ) Interview success probability | DS AS MLE PDS | Application process | Application prep | Easy | ||
| ( Login required ) Offer validity and cooling-off periods | DS AS MLE PDS | Application process | Application prep | Easy | ||
| ( Login required ) Referrals vs. online applications | DS AS MLE PDS | Application process | Application prep | Easy | ||
| ( Login required ) Response time | DS AS MLE PDS | Application process | Application prep | Easy | ||
| ( Login required ) Resume review | DS AS MLE PDS | Application process | Application prep, Resume | Easy | ||
| ( Login required ) Timeline: from application to offer | DS AS MLE PDS | Application process | Application prep | Easy | ||
| ( Login required ) Up-level during interview | DS AS MLE PDS | Application process | Application prep | Easy | ||
| ( Login required ) K Closest Points to Origin (Leetcode 973) | DS AS MLE | Data Structures and Algorithms | Heap | Easy | ||
| ( Login required ) Longest substring without repeating characters (Leetcode 3) | DS AS MLE | Data Structures and Algorithms | Hash, Sliding Window | Medium | Amazon | |
| ( Login required ) Number of recent calls (Leetcode 933) | DS AS MLE | Data Structures and Algorithms | Queue, DEQueue | Easy | ||
| ( Login required ) Analyze prediction error | DS AS MLE | Machine Learning | Prediction error, Bias-variance tradeoff, Diagnostics, Learning curves | Medium | Google Salesforce Snap Thumbtack | |
| ( Login required ) AUC ROC and predicted output transformations | DS AS MLE | Machine Learning | AUC ROC | Easy | ||
| ( Login required ) Sample from random generator | DS AS MLE | Machine Learning Coding | Sample, Uniform, Random number generator | Medium | Google Snap | |
| ( Login required ) Simulate dynamic coin flips | DS AS MLE | Machine Learning Coding | Simulation | Easy | Cruise | |
| ( Login required ) Encoding categorical features | PDS DS AS MLE | Machine Learning | Categorical features, Embeddings, One-hot encoding, Hashing | Easy | Apple Hinge Thumbtack | |
| ( Login required ) Example of high-bias and high variance | DS AS MLE | Machine Learning | Bias-variance tradeoff | Medium | ||
| ( Login required ) k-means | PDS DS AS MLE | Machine Learning | k-means | Medium | ||
| ( Login required ) L1 (Lasso) vs. L2 (Ridge) regularization | DS AS MLE | Machine Learning | L1, L2, Redularization, Lasso, Ridge | Medium | Instacart Uber | |
| ( Login required ) Linear regression likelihood function | DS AS MLE | Machine Learning | Linear regression, Likelihood function, Formula derivation | Medium | Uber | |
| ( Login required ) Model interpretability | DS AS MLE | Machine Learning | Interpretability | Easy | Paylocity | |
| ( Login required ) Multiclass evaluation metrics | DS AS MLE | Machine Learning | Multiclass, Diagnostics | Medium | Fidelity Paypal | |
| ( Login required ) NDCG vs. Mean Average Precision (MAP) vs. Mean Reciprocal Rank (MRR) | AS MLE | Machine Learning | Ranking metrics, NDCG, Mean average precision (MAP), Mean Reciprocal Rank (MRR), Recommender Systems | Medium | Apple Coursera Indeed Paypal | |
| ( Login required ) Range of R2 when combining regressions | PDS DS AS MLE | Machine Learning | Linear regression, Goodness of fit, R-squared, Correlation | Medium | D.E. Shaw | |
| ( Login required ) ROC vs. PR curve | AS MLE | Machine Learning | AUC, ROC, AUPR, Precision, Recall, Evaluation metrics | Medium | Amazon Google Meta Reddit | |
| ( Login required ) Measuring counterfactual impact | DS PDS | Metrics | Problem solving, Counterfactual | Hard | Meta Snap | |
| ( Login required ) Games between two players | DS PDS AS | Probability theory | Recursive relationship | Medium | Amazon | |
| ( Login required ) Monty Hall | DS PDS AS | Probability theory | Bayes rule, Conditional independence, Prior evidence | Medium | ||
| ( Login required ) Red and blue balls | DS PDS AS | Probability theory | Counting, Combinations, Repetition, Binomial | Easy | Indeed | |
| ( Login required ) Sum of normally distributed random variables | DS PDS AS | Probability theory | Normal, PDF, CDF | Medium | Meta | |
| ( Login required ) Two children I | DS PDS AS | Probability theory | Prior evidence | Easy | Amazon | |
| ( Login required ) Unfair coin probability | DS PDS AS | Probability theory | Bayes rule, Conditional probability | Easy | ||
| ( Login required ) Choose house or techno | DS AS PDS | SQL | Logical OR | Easy | ||
| ( Login required ) Expensive house songs I | DS AS PDS | SQL | Subquery, CTE, Join | Medium | ||
| ( Login required ) Expensive house songs II | DS AS PDS | SQL | Subquery, CTE, Join, Window functions | Hard | Google Snap Tik Tok | |
| ( Login required ) Histogram of songs | DS AS PDS | SQL | Recursive CTE, Join | Hard | ||
| ( Login required ) Monthly Active Users (MAU) | DS AS PDS | SQL | Aggregation | Easy | ||
| ( Login required ) Songs that did not enter the charts or entered high | DS AS PDS | SQL | Subquery, Join | Medium | ||
| ( Login required ) Songs that ranked 1 to 50 | DS AS PDS | SQL | Between | Easy | ||
| ( Login required ) Songs that stay in the chars for a while | DS AS PDS | SQL | Subquery, CTE, Join, ALL, Window functions | Medium | ||
| ( Login required ) Biased coin | DS PDS AS | Statistics | Expectation, CLT, Binomial, Normal, Bernoulli, Hypothesis testing, CDF | Medium | Meta | |
| ( Login required ) Gambler’s ruin win probability | DS PDS AS | Statistics | Gambler ruin, Random walk, Expectation | Medium | Meta | |
| ( Login required ) Manual estimation of flips | DS PDS AS | Statistics | Normal, CDF, Binomial, CLT | Medium | ||
| ( Login required ) Measuring sticks | DS PDS AS | Statistics | Variance | Medium | Sisu | |
| ( Login required ) Monotonic draws | DS PDS AS | Statistics | Expectation | Hard | ||
| ( Login required ) Prussian horses | DS PDS AS | Statistics | Poisson, Hypothesis testing, CDF | Medium | Indeed | |
| ( Login required ) Relationship between p-val and confidence interval | DS PDS AS | Statistics | Confidence interval, P-value, Hypothesis testing | Easy | ||
| ( Login required ) Questions worksheet (deep dive) | DS AS MLE PDS | Technical deep dive | Discussion prep | Hard | ||
| ( Subscription required ) AA tests | DS PDS AS | A/B Testing | Variance | Easy | Apple Snap Uber | |
| ( Subscription required ) Counterfactual definition | DS PDS AS | A/B Testing | Counterfactual | Easy | ||
| ( Subscription required ) Equal-sized treatment and control groups | DS PDS AS | A/B Testing | Power, Variance, Sample size | Medium | Snap | |
| ( Subscription required ) False discovery control | DS PDS AS | A/B Testing | False discovery rate, Multiple hypotheses testing, Benjamini & Hochberg, Bonferroni | Easy | Discord Pinterest | |
| ( Subscription required ) Interference | DS PDS AS | A/B Testing | Interference | Easy | Snap TaskRabbit Uber | |
| ( Subscription required ) Multi-armed and contextual bandits in AB testing | DS PDS AS | A/B Testing | Contextual bandits, Multi-armed bandits | Medium | ||
| ( Subscription required ) Novelty and primacy effects | DS PDS AS | A/B Testing | Novelty effects, Primacy effects | Easy | Uber | |
| ( Subscription required ) Randomization checks | DS PDS AS | A/B Testing | Randomization | Easy | ||
| ( Subscription required ) Randomization level | DS PDS AS | A/B Testing | Randomization, Variance | Medium | ||
| ( Subscription required ) Climbing stairs (Leetcode 70) | DS AS MLE | Data Structures and Algorithms | Recursion, Dynamic programming | Easy | ||
| ( Subscription required ) Find if Path Exists in Graph (Leetcode 1971) | DS AS MLE | Data Structures and Algorithms | DFS, BFS | Medium | Amazon | |
| ( Subscription required ) Search in Binary Search Tree (Leetcode 700) | DS AS MLE | Data Structures and Algorithms | Binary Search, Binary Search Tree | Easy | ||
| ( Subscription required ) Sort an Array (Leetcode 912) | DS AS MLE | Data Structures and Algorithms | Recursion, Sorting | Medium | ||
| ( Subscription required ) Activation functions | AS MLE | Machine Learning | Neural networks, Deep learning | Easy | Cruise | |
| ( Subscription required ) Active learning | AS MLE | Machine Learning | Labels, Label sampling | Medium | Dropbox | |
| ( Subscription required ) Adagrad vs. RMSProp vs. Adam | AS MLE | Machine Learning | Neural networks, Deep learning, Optimization | Hard | Apple Cruise Instacart | |
| ( Subscription required ) Adaptive learning rate | AS MLE | Machine Learning | Neural networks, Deep learning, Optimization | Hard | Cruise Instacart Two Sigma | |
| ( Subscription required ) Approximate nearest neighbors | AS MLE | Machine Learning | ANN, ANNOY, Nearest neighbors | Medium | Dropbox Lacework | |
| ( Subscription required ) Attention (intuition) | AS MLE | Machine Learning | Neural networks, Deep learning, Transformers, LLMs | Easy | Hinge Lacework Tik Tok | |
| ( Subscription required ) Baselines | AS MLE DS | Machine Learning | Model evaluation | Easy | ||
| ( Subscription required ) Bayesian frequentist statistics | AS MLE | Machine Learning | Bayesian, Frequentist | Easy | ||
| ( Subscription required ) Bias-variance biased estimator | DS AS MLE | Machine Learning | Bias-variance tradeoff | Medium | ||
| ( Subscription required ) Bootstrap | PDS DS AS MLE | Machine Learning | Bootstrap | Easy | Google LinkedIn | |
| ( Subscription required ) K-means from scratch | DS AS MLE | Machine Learning Coding | k-means | Medium | Amazon Etsy Snap | |
| ( Subscription required ) Linear regression with stochastic gradient descent | DS AS MLE | Machine Learning Coding | Stochastic Gradient descent, Linear regression | Medium | ||
| ( Subscription required ) Logistic regression with gradient descent | DS AS MLE | Machine Learning Coding | Gradient descent, Logistic regression | Medium | ||
| ( Subscription required ) Naive Bayes from scratch | DS AS MLE | Machine Learning Coding | Gaussian Naive Bayes | Medium | Hinge | |
| ( Subscription required ) Neural network implementation | AS MLE | Machine Learning Coding | Gradient descent, Neural networks, Neuron | Hard | Uber | |
| ( Subscription required ) Principal Component Analysis (PCA) from scratch | DS AS MLE | Machine Learning Coding | Principal Component Analysis (PCA) | Medium | ||
| ( Subscription required ) Common causes of data leakage | DS AS MLE | Machine Learning | Data leakage | Medium | ||
| ( Subscription required ) Comparing decision trees with random forests | DS AS MLE | Machine Learning | Decision trees, Random forests | Easy | Paypal | |
| ( Subscription required ) Correlation with binary variables | DS AS MLE | Machine Learning | Correlation, Hypothesis testing, Point-biserial correlation coefficient | Easy | Meta | |
| ( Subscription required ) Cross validation | PDS DS AS MLE | Machine Learning | Cross validation, Offline evaluation | Easy | Amazon Amperity Discord | |
| ( Subscription required ) Decide between a multinomial vs. a binary modeling approach | AS MLE | Machine Learning | Modeling, Multinomial, Binary | Easy | ||
| ( Subscription required ) Discretization drawbacks | DS AS MLE | Machine Learning | Categorical variables, Discretization | Easy | ||
| ( Subscription required ) Ensembles | AS MLE DS | Machine Learning | Ensembles, Numerical example | Medium | ||
| ( Subscription required ) Examples of encoder and decoder models | AS MLE | Machine Learning | LLMs, Transformers, Encoder, Decoder | Easy | Fidelity Salesforce | |
| ( Subscription required ) Exponentially weighted moving average | AS MLE | Machine Learning | Exponentially weighted moving average, Formula derivation, Proof | Medium | ||
| ( Subscription required ) Feature crossing | AS MLE | Machine Learning | Feature engineering, Deep learning | Easy | Meta | |
| ( Subscription required ) Feature engineering in the era of deep learning | DS AS MLE | Machine Learning | Feature engineering | Easy | ||
| ( Subscription required ) Gini impurity vs. information gain | AS MLE | Machine Learning | Decision tree, Information Gain, Gini impurity | Medium | ||
| ( Subscription required ) Gradient boosting vs. random forests | DS AS MLE | Machine Learning | Gradient boosting, Random forests, Bagging, Boosting | Medium | Indeed Instacart Meta Salesforce | |
| ( Subscription required ) Gradient descent vs. Stochastic Gradient descent and local minima | AS MLE | Machine Learning | Gradient descent (GD), Stochastic gradient descent (SGD), Local minima, Optimization, Deep learning | Medium | Cruise Discord Google Indeed | |
| ( Subscription required ) Gradient descent vs. Stochastic Gradient descent and learning rate | AS MLE | Machine Learning | Gradient descent (GD), Stochastic gradient descent (SGD), Learning rate, Optimization, Deep learning | Medium | Paypal | |
| ( Subscription required ) Predict whether a movie will receive good reviews | AS MLE | Machine Learning Hands on | Feature engineering, Data exploration, ML modeling, Logistic regression, One hot encoding | Hard | ||
| ( Subscription required ) How to get more labels | AS MLE | Machine Learning | Modeling, Label encoding | Medium | ||
| ( Subscription required ) Hypothesis testing in regression coefficients | DS AS MLE | Machine Learning | Linear regression, Hypothesis testing | Medium | ||
| ( Subscription required ) Information gain in decision trees | AS MLE | Machine Learning | Decision tree, Entroy, Information Gain, Formula derivation | Medium | ||
| ( Subscription required ) Intercept | PDS DS AS MLE | Machine Learning | Linear regression, Intercept | Easy | ||
| ( Subscription required ) Interpretability | PDS DS AS MLE | Machine Learning | ML interpretability | Easy | Apple | |
| ( Subscription required ) L2 regularization vs. weight decay | AS MLE | Machine Learning | Neural networks, Deep learning, Regularization | Hard | Apple Google Instacart ROKT | |
| ( Subscription required ) Linear regression assumptions | PDS DS AS MLE | Machine Learning | Linear regression | Easy | Fidelity | |
| ( Subscription required ) Linear regression with duplicated rows | DS AS MLE | Machine Learning | Linear regression, Statistical significance | Easy | ||
| ( Subscription required ) Linear regression with stochastic gradient descent (formula derivation) | AS MLE | Machine Learning | Linear regression, Stochastic gradient descent, Formula derivation | Medium | ||
| ( Subscription required ) Logistic regression and standardization | DS AS MLE | Machine Learning | Logistic regression, Standardization | Easy | Indeed Paypal | |
| ( Subscription required ) Logistic regression assumptions | PDS DS AS MLE | Machine Learning | Logistic regression | Easy | Indeed Paypal Warner Bros | |
| ( Subscription required ) Minimization of loss function intuition | AS MLE | Machine Learning | Neural networks, Deep learning, Optimization | Easy | Robinhood Uber | |
| ( Subscription required ) Missing data | PDS DS AS MLE | Machine Learning | Missing data | Easy | Apple Flatiron Health | |
| ( Subscription required ) Momentum | AS MLE | Machine Learning | Neural networks, Deep learning, Optimization | Hard | Apple Cruise Instacart Paypal | |
| ( Subscription required ) MSE vs. MAE | PDS DS AS MLE | Machine Learning | MSE, MAE | Easy | ||
| ( Subscription required ) Multi-headed attention and self attention | AS MLE | Machine Learning | Neural networks, Deep learning, Transformers, LLMs | Medium | Amazon Google | |
| ( Subscription required ) Multicollinearity | PDS DS AS MLE | Machine Learning | Multicollinearity, Linear regression | Medium | Paypal | |
| ( Subscription required ) Negative sampling | AS MLE | Machine Learning | Neural networks, Deep learning, Negative sampling | Medium | Dropbox Reddit | |
| ( Subscription required ) Neuaral networks in layman terms | AS MLE | Machine Learning | Neural networks, Deep learning | Easy | Fidelity | |
| ( Subscription required ) Non-probability sampling | DS AS MLE | Machine Learning | Sampling, Non-probability | Easy | ||
| ( Subscription required ) Normalization in neural networks | AS MLE | Machine Learning | Neural networks, Deep learning, Batch normalization, Layer normalization | Medium | Amazon Google Tinder | |
| ( Subscription required ) Normalization vs. Standardization | PDS DS AS MLE | Machine Learning | Linear regression, Standardization, Normalization | Easy | ||
| ( Subscription required ) Not enough data to train a model | DS AS MLE | Machine Learning | Data limitations | Easy | ||
| ( Subscription required ) Optimize multiple objectives | AS MLE | Machine Learning | Modeling, Multiple objectives | Easy | ||
| ( Subscription required ) Outliers | PDS DS AS MLE | Machine Learning | Outliers, Cook’s distance, Regularization | Easy | ||
| ( Subscription required ) Overfitting in neural networks | AS MLE | Machine Learning | Neural networks, Deep learning, Overfitting | Medium | Apple Google Indeed Instacart | |
| ( Subscription required ) Positional embeddings | AS MLE | Machine Learning | Feature engineering, Deep learning, Transformers, LLMs, Positional embeddings, Positional encodings | Hard | ||
| ( Subscription required ) Principal Component Analysis (PCA) | PDS DS AS MLE | Machine Learning | PCA | Easy | ||
| ( Subscription required ) Prove that a median minizes MAE | AS MLE | Machine Learning | MAE, Median, Formula derivation, Proof | Hard | LinkedIn Uber | |
| ( Subscription required ) Random forest feature importance | AS MLE | Machine Learning | Feature importance, Explainability, Gini importance, Permutation importance | Medium | Discord Grammarly Hinge | |
| ( Subscription required ) Random vs. stratified sampling | PDS DS AS MLE | Machine Learning | Sampling, Stratified sampling | Easy | Meta | |
| ( Subscription required ) Self-supervised learning | AS MLE | Machine Learning | Neural networks, Deep learning, Contrastive learning | Easy | Dropbox Reddit | |
| ( Subscription required ) SMOTE | AS MLE | Machine Learning | Imbalanced classification, SMOTE, Data augmentation | Easy | Paypal Robinhood Snap Thumbtack | |
| ( Subscription required ) API patterns | MLE | Machine Learning System Design | APIs, GraphQL, REST | Easy | Lacework | |
| ( Subscription required ) Build an ML system to predict Ad clicks | AS MLE | Machine Learning System Design | ML system design, Feature engineering, Data exploration, ML modeling, Monitoring, Deployment, Business metrics | Hard | Meta | |
| ( Subscription required ) Cloud vs. on-device deployment | MLE | Machine Learning System Design | Deployment, Cloud, Edge | Medium | ||
| ( Subscription required ) Complex vs. simple deployment | MLE | Machine Learning System Design | Deployment | Easy | ||
| ( Subscription required ) Crons, schedulers, orchestrattors | MLE | Machine Learning System Design | ML infra | Medium | Dropbox | |
| ( Subscription required ) Data, model, and pipeline parallelism | MLE | Machine Learning System Design | Parallelism | Medium | ||
| ( Subscription required ) Debug an ML model | MLE | Machine Learning System Design | Best practices | Medium | ||
| ( Subscription required ) How to speed up inference | MLE | Machine Learning System Design | Inference | Easy | Dropbox Grammarly Robinhood Uber | |
| ( Subscription required ) ML system design tools and use cases | MLE | Machine Learning System Design | ML infra, CDN, Kafka, Reddis, Dynamo, Cassandra, Chubby, PGVector, DBT, Feast, MLFlow, Statsig, Airflow, Fiddler | Hard | Hinge Reddit | |
| ( Subscription required ) Online prediction, vs. batch prediction | MLE | Machine Learning System Design | Inference | Medium | Dropbox Reddit Uber | |
| ( Subscription required ) Simple model deployment process | MLE | Machine Learning System Design | Deployment, Docker | Easy | ||
| ( Subscription required ) Training tracking | MLE | Machine Learning System Design | Best practices | Medium | ||
| ( Subscription required ) Types of data distribution shifts | MLE | Machine Learning System Design | Train-serving skew, Covariate shift, Label shift, Concept shift | Medium | Dropbox Lacework | |
| ( Subscription required ) Transfer learning | AS MLE | Machine Learning | Neural networks, Deep learning, Transformers, LLMs, Catastrophic forgetting | Easy | ||
| ( Subscription required ) Transfer learning vs. knowledge distillation | AS MLE | Machine Learning | LLM, Deep learning, Transfer learning, Knowledge distillation | Medium | ||
| ( Subscription required ) Vanishing and exploding gradients (mathematical explaination) | AS MLE | Machine Learning | Neural networks, Deep learning, Mathematical explaination | Hard | Robinhood Salesforce | |
| ( Subscription required ) Weight initialization | AS MLE | Machine Learning | Neural networks, Deep learning | Medium | Tinder | |
| ( Subscription required ) Weighted and importance sampling | DS AS MLE | Machine Learning | Sampling, Weighted sampling, Importance sampling | Easy | ||
| ( Subscription required ) Characteristics of metrics | DS PDS | Metrics | Characteristics of metrics | Easy | Duolingo Snap | |
| ( Subscription required ) Types of metrics | DS PDS | Metrics | Types of metrics | Easy | ||
| ( Subscription required ) Consecutive tails | DS PDS AS | Probability theory | Permutations, Repetition | Easy | ||
| ( Subscription required ) Largest number rolled | DS PDS AS | Probability theory | Counting, Permutations, Repetition | Medium | ||
| ( Subscription required ) Median probability | DS PDS AS | Probability theory | Binomial, Uniform, CDF | Medium | Meta | |
| ( Subscription required ) Number of emails | DS PDS AS | Probability theory | Poisson distribution | Easy | Cruise | |
| ( Subscription required ) Paths to destination | DS PDS AS | Probability theory | Counting, Combinations | Easy | ||
| ( Subscription required ) Repeated rolls until 4 | DS PDS AS | Probability theory | Geometric distribution | Easy | ||
| ( Subscription required ) Sample digits 1-10 | DS PDS AS | Probability theory | Sample from samples, Uniform | Medium | Compass Google Snap | |
| ( Subscription required ) Two fair die rolls | DS PDS AS | Probability theory | Independence, CDF, PMF | Easy | ||
| ( Subscription required ) Artists with more songs than others | DS AS PDS | SQL | Subquery, CTE, Join, Window functions | Hard | Meta Snap Tik Tok | |
| ( Subscription required ) Concat columns | DS AS PDS | SQL | Concat | Easy | ||
| ( Subscription required ) Engagement Score by User | DS AS PDS | SQL | CTEs, WINDOW FUNCTION | Hard | ||
| ( Subscription required ) Follower-Following Ratios | DS AS PDS | SQL | CTEs, Aggregation | Medium | ||
| ( Subscription required ) Label recent songs | DS AS PDS | SQL | Case | Easy | ||
| ( Subscription required ) Median songs per artist | DS AS PDS | SQL | CTE, Window functions | Hard | ||
| ( Subscription required ) Most Liked Content Per User | DS AS PDS | SQL | Join, Window Function, CTE | Medium | ||
| ( Subscription required ) Songs in charts with greater durations | DS AS PDS | SQL | Subquery, CTE, Join, Window functions | Hard | ||
| ( Subscription required ) Songs per genre | DS AS PDS | SQL | Group by | Easy | ||
| ( Subscription required ) Songs with letters | DS AS PDS | SQL | Regexp | Easy | ||
| ( Subscription required ) An intuitive way to write power | DS PDS AS | Statistics | Power, Hypothesis testing | Easy | ||
| ( Subscription required ) Buy and sell stocks | DS PDS AS | Statistics | Gambler ruin, Expectation, Recursion, Random walk | Medium | D.E. Shaw | |
| ( Subscription required ) CI of flipping heads | DS PDS AS | Statistics | Confidence Interval, CLT, Bernoulli trials | Medium | ||
| ( Subscription required ) Confidence interval definition | DS PDS AS | Statistics | Confidence interval, Hypothesis testing | Easy | Meta | |
| ( Subscription required ) Confidence intervals that overlap | DS PDS AS | Statistics | Confidence interval, Hypothesis testing | Medium | ||
| ( Subscription required ) Covariance of dependent variables | DS PDS AS | Statistics | Variance, Uniform, Covariance, Expectation | Medium | ||
| ( Subscription required ) Distribution of a CDF | DS PDS AS | Statistics | CDF, Inverse transform | Medium | ||
| ( Subscription required ) Dynamic coin flips | DS PDS AS | Statistics | Expectation, Simulation | Hard | Cruise | |
| ( Subscription required ) Expected number of consecutive heads | DS PDS AS | Statistics | Expectation | Medium | ||
| ( Subscription required ) Number of draws to get greater than 1 | DS PDS AS | Statistics | Normal, Geometric, CDF, Expectation | Medium | ||
| ( Subscription required ) P-value definition | DS PDS AS | Statistics | P-value, Hypothesis testing | Easy | Google Meta Pinterest Snap | |
| ( Subscription required ) Tests for normality | DS PDS AS | Statistics | Hypothesis testing, Normality | Easy | Duolingo Snap |