Data Science Tests

Batch Vs Realtime — Advanced

Advanced batch vs streaming design: exactly-once semantics, stream-table duality, dual pipelines, state scaling, cost/latency tradeoffs, and operational failure modes. Mix of single-answer and multiple-answer items; multi-select questions ask you to select every correct statement.

Batch Vs Realtime — Intermediate

Choosing and operating batch vs streaming paths: windows, watermarks, delivery semantics, backpressure, CDC, and serving fresh features. Mix of single-answer and multiple-answer items; multi-select questions ask you to select every correct statement.

Batch Vs Realtime — Beginner

Foundations of batch and real-time data processing: latency, scheduling, use cases, and how pipelines differ. Mix of single-answer and multiple-answer items; multi-select questions ask you to select every correct statement.

Data Drift — Advanced

Advanced data drift for production ML: multivariate and embedding drift, MMD and domain classifiers, concept vs covariate shift under delay, causal pitfalls, and governance of retraining. Mix of single-answer and multiple-answer items; multi-select questions ask you to select every correct statement.

Data Drift — Intermediate

Operational data drift detection: PSI and statistical tests, feature vs prediction drift, windowing, alert design, and retraining triggers. Mix of single-answer and multiple-answer items; multi-select questions ask you to select every correct statement.

Data Drift — Beginner

Foundations of data drift in ML: covariate shift, monitoring live inputs against a reference baseline, and why production distributions change. Mix of single-answer and multiple-answer items; multi-select questions ask you to select every correct statement.

Model Registry — Advanced

Advanced model registry governance: multi-region replication, model cards, supply-chain attestations, shadow/canary patterns, federated registration, and enterprise audit design. Mix of single-answer and multiple-answer items; multi-select questions ask you to select every correct statement.

Model Registry — Intermediate

Operational model registry practice: aliases and champions, promotion gates, signatures, access control, CI/CD handoffs, and comparing registered versions before production. Mix of single-answer and multiple-answer items; multi-select questions ask you to select every correct statement.

Model Registry — Beginner

Foundations of ML model registries: versioning, stages, artifacts, lineage, and why teams centralize models instead of scattering files. Mix of single-answer and multiple-answer items; multi-select questions ask you to select every correct statement.

Experiment Platforms — Advanced

Sequential testing, interference, stratified bucketing, bot filtering, portfolio holdouts, cross-platform migration, and governance at scale on experimentation infrastructure.

Experiment Platforms — Intermediate

Layers and holdouts, targeting and ramps, sticky assignment, sample ratio checks, warehouse exports, and operational hygiene on experimentation platforms. Mix of single-answer and multiple-answer items.

Experiment Platforms — Beginner

How A/B and feature-flag platforms assign users, log exposures, allocate traffic, and support safe rollout basics. Mix of single-answer and multiple-answer items; multi-select questions ask you to select every correct statement.

Power Analysis — Advanced

Advanced power for experimentation programs: simulation-based power, cluster and geo designs, multiplicity, sequential and optional stopping, variance reduction, and aligning analysis models with power assumptions. Mix of single-answer and multiple-answer items; multi-select questions ask you to select every correct statement.

Power Analysis — Intermediate

Planning and interpreting power for product experiments: effect sizes, variance, two-proportion and mean tests, one- vs two-sided tests, and minimum detectable effects. Mix of single-answer and multiple-answer items; multi-select questions ask you to select every correct statement.

Power Analysis — Beginner

Foundations of statistical power: Type I/II errors, significance level, sample size intuition, and why underpowered experiments miss real effects. Mix of single-answer and multiple-answer items; multi-select questions ask you to select every correct statement.

Causal Inference Basics — Advanced

Identification vs estimation, SUTVA, heterogeneous effects, mediation, double robustness, staggered DiD cautions, and design-first causal reasoning for complex product and policy settings. Mix of single-answer and multiple-answer items.

Causal Inference Basics — Intermediate

DAGs and backdoor adjustment, regression pitfalls, diff-in-diff intuition, instrumental variables, matching, and threats to validity in quasi-experiments. Mix of single-answer and multiple-answer items.

Causal Inference Basics — Beginner

Foundations of cause and effect: correlation vs causation, confounding, randomization, counterfactuals, and common observational pitfalls. Mix of single-answer and multiple-answer items.

Feature Engineering — Advanced

Advanced feature engineering: temporal and nested-CV leakage, train–serve skew, entity embeddings, automated interactions, monotonic constraints, drift monitoring, null importance, and causal vs predictive features. Mix of single-answer and multiple-answer items; multi-select questions ask you to select every correct statement.

Feature Engineering — Intermediate

Intermediate feature engineering: encoding high-cardinality categories, binning, interactions, point-in-time aggregates, cross-validated encoders, multicollinearity, and feature selection trade-offs. Mix of single-answer and multiple-answer items; multi-select questions ask you to select every correct statement.

Feature Engineering — Beginner

Foundations of feature engineering: raw inputs vs model features, numeric and categorical handling, basic encoding, scaling, missing values, and avoiding obvious leakage. Mix of single-answer and multiple-answer items; multi-select questions ask you to select every correct statement.

Model Evaluation — Advanced

Advanced ML evaluation: nested CV pitfalls, calibration error metrics, fairness-aware reporting, bootstrap and paired tests, off-policy and slice analysis, and probabilistic forecasting scores. Mix of single-answer and multiple-answer items; multi-select questions ask you to select every correct statement.

Model Evaluation — Intermediate

Intermediate ML evaluation: stratified splits, precision–recall tradeoffs, macro vs micro metrics, calibration basics, learning curves, threshold tuning, and regression diagnostics. Mix of single-answer and multiple-answer items; multi-select questions ask you to select every correct statement.

Model Evaluation — Beginner

Foundations of ML model evaluation: train/validation/test splits, confusion matrix, precision and recall, overfitting, and choosing sensible baselines. Mix of single-answer and multiple-answer items; multi-select questions ask you to select every correct statement.

Supervised Learning — Advanced

Advanced supervised ML: calibration, class hierarchies, multi-label vs multiclass, sample weights, label smoothing, distillation, ordinal and cost-sensitive learning, kernel methods, and production pitfalls. Mix of single-answer and multiple-answer items; multi-select questions ask you to select every correct statement.

Supervised Learning — Intermediate

Intermediate supervised ML: regularization, class imbalance, decision boundaries, logistic and softmax outputs, feature scaling, encoding categoricals, tree-based learners, and training pitfalls. Mix of single-answer and multiple-answer items; multi-select questions ask you to select every correct statement.

Supervised Learning — Beginner

Foundations of supervised machine learning: labeled data, classification vs regression, training and prediction, common losses, overfitting, and basic model families. Mix of single-answer and multiple-answer items; multi-select questions ask you to select every correct statement.

Sampling Bias — Advanced

Selection mechanisms, colliders, left truncation, inverse probability weighting, Heckman-type correction, nonignorable nonresponse, transportability, and sensitivity analysis for biased samples. Mix of single-answer and multiple-answer items; multi-select questions ask you to select every correct statement.

Sampling Bias — Intermediate

Coverage error, quota and panel designs, attrition, length-biased and Berkson-type selection, healthy-worker effects, weighting limits, and diagnosing bias in observational samples. Mix of single-answer and multiple-answer items; multi-select questions ask you to select every correct statement.

Sampling Bias — Beginner

Foundations of sampling bias: target populations, sampling frames, convenience and volunteer samples, non-response, survivorship, and limits on generalization. Mix of single-answer and multiple-answer items; multi-select questions ask you to select every correct statement.

Inferential Stats — Advanced

Advanced inferential statistics: likelihood and MLE intuition, bootstrap, permutation tests, Bayesian vs frequentist contrasts, mixed models, nonparametrics, Simpson’s paradox in inference, and critique of p-hacking. Mix of single-answer and multiple-answer items; multi-select questions ask you to select every correct statement.

Inferential Stats — Intermediate

Intermediate inferential statistics: power and sample size, multiple comparisons, ANOVA intuition, chi-square tests, paired designs, assumption diagnostics, and interpreting CIs vs tests. Mix of single-answer and multiple-answer items; multi-select questions ask you to select every correct statement.

Inferential Stats — Beginner

Foundations of inferential statistics: populations vs samples, estimation, confidence intervals, hypothesis testing basics, p-values, and common test families. Mix of single-answer and multiple-answer items; multi-select questions ask you to select every correct statement.

Descriptive Stats — Advanced

Advanced descriptive statistics: Simpson’s paradox in aggregates, robust and transformed summaries, quantiles and winsorization, bivariate association limits, multimodal distributions, and reporting choices. Mix of single-answer and multiple-answer items; multi-select questions ask you to select every correct statement.

Descriptive Stats — Intermediate

Intermediate descriptive statistics: variance and standard deviation, IQR and box plots, weighted means, z-scores, correlation as association, and choosing summaries under skew or outliers. Mix of single-answer and multiple-answer items; multi-select questions ask you to select every correct statement.

Descriptive Stats — Beginner

Foundations of descriptive statistics: measures of center and spread, tables and charts, variable types, and reading summaries without inferential claims. Mix of single-answer and multiple-answer items; multi-select questions ask you to select every correct statement.