Data science is one of the fastest-growing fields in tech, demanding proficiency across statistics, programming, machine learning, and communication. Whether you're preparing for a certification exam like the IBM Data Science Professional Certificate, Google Data Analytics Certificate, or Databricks Certified Associate Developer, or getting ready for a technical interview, structured practice is essential.
A PDF practice test gives you the flexibility to study offline, annotate questions, and revisit problem areas at your own pace. You can print it out, work through it during a commute, or use it as a timed mock exam — all without needing an internet connection.
Key skills tested in data science assessments include statistical reasoning, Python and R programming, machine learning model building, SQL querying, and data visualization. Employers and certification bodies want to see that you can not only write code but also think critically about data pipelines, model evaluation, and real-world deployment challenges.
This free PDF download covers all the major domains you'll encounter in exams and interviews. Use it alongside hands-on projects and our online practice tests to build confidence and close knowledge gaps before test day.
Statistics is the backbone of data science. You need a solid grasp of probability distributions — normal, binomial, Poisson, and exponential — and when each applies. Hypothesis testing is central to data-driven decision making: understand null and alternative hypotheses, p-values, Type I and Type II errors, and the difference between one-tailed and two-tailed tests.
Confidence intervals appear in both certification exams and interview whiteboard sessions. Know how to construct a 95% confidence interval for a mean and explain what it means in plain English. Don't overlook effect size alongside statistical significance — a result can be statistically significant but practically meaningless with large enough sample sizes.
Bayesian statistics is increasingly tested. Understand Bayes' theorem, prior vs. posterior distributions, and how Bayesian approaches differ from frequentist methods. Topics like A/B testing, multi-armed bandits, and sequential analysis all build on this foundation.
Python dominates data science tooling. NumPy provides the numerical computing base — array operations, broadcasting, vectorized math, and random number generation. Know how to manipulate multi-dimensional arrays efficiently without Python-level loops.
pandas is the workhorse for tabular data. You should be able to load CSVs, merge DataFrames, handle missing values, apply groupby aggregations, use pivot tables, and work with DatetimeIndex for time series. Interview questions often focus on chaining operations and avoiding anti-patterns like iterrows().
scikit-learn ties the machine learning workflow together. Practice the fit/transform/predict API, understand train/test splits vs. cross-validation, and know how to use Pipeline objects to prevent data leakage. Regularization parameters, feature scaling, and hyperparameter tuning with GridSearchCV or RandomizedSearchCV are common exam topics.
Linear and logistic regression are tested heavily — understand the cost function, gradient descent, regularization (L1 Lasso vs. L2 Ridge), and interpreting coefficients. Know when multicollinearity is a problem and how to detect it using VIF.
Tree-based methods are interview favorites. Decision trees suffer from high variance, which Random Forests address through bagging. Gradient Boosting (XGBoost, LightGBM, CatBoost) reduces bias iteratively. Know the difference between bagging and boosting philosophically, not just by name.
Unsupervised learning includes K-Means clustering (elbow method for choosing K, sensitivity to initialization), hierarchical clustering (dendrograms, linkage methods), and dimensionality reduction with PCA. Understand the explained variance ratio and when to use PCA vs. t-SNE vs. UMAP for visualization.
Classification tasks require understanding precision, recall, F1 score, and the AUC-ROC curve. Imbalanced datasets call for SMOTE, class weighting, or threshold adjustment — not just accuracy.
SQL proficiency is non-negotiable for data science roles. Master the core JOIN types (INNER, LEFT, RIGHT, FULL OUTER) and know when to use subqueries vs. CTEs for readability. Window functions — ROW_NUMBER(), RANK(), DENSE_RANK(), LAG(), LEAD(), and partitioned aggregations — appear in nearly every data science interview.
Aggregation with GROUP BY and HAVING, date functions, and string manipulation are standard. Query optimization matters at scale: understand indexes, EXPLAIN plans, and why SELECT * is inefficient on large tables. Practice writing recursive CTEs for hierarchical data and self-joins for cohort analysis.
Real-world data is messy. Data cleaning typically takes 60–80% of a data science project's time. Common tasks include handling missing values (imputation strategies: mean, median, mode, KNN imputation, or dropping rows), removing duplicates, correcting data types, and standardizing categorical encodings.
Outlier detection methods include Z-score thresholding, IQR fences, and isolation forests for multivariate cases. Know when to remove outliers vs. transform them (log transformation for skewed distributions). Feature engineering — polynomial features, interaction terms, target encoding, date decomposition — can dramatically improve model performance.
Choosing the right chart type is a skill. Use scatter plots for correlations, histograms for distributions, box plots for comparing groups, and heatmaps for correlation matrices. Bar charts beat pie charts for comparisons; line charts are for time series.
With matplotlib, understand the figure/axes hierarchy and customize titles, labels, legends, and color schemes. seaborn provides a higher-level API with built-in statistical summaries — know pairplot, heatmap, and FacetGrid. For dashboarding, Tableau and Power BI are tested in analyst roles; understand how to build calculated fields and publish to Tableau Server.
Neural network fundamentals are increasingly expected. Understand perceptrons, activation functions (ReLU, sigmoid, softmax), forward and back propagation, and gradient descent variants (SGD, Adam, RMSprop). Know what overfitting looks like in training curves and how dropout, batch normalization, and early stopping address it.
Convolutional neural networks (CNNs) are standard for image data; recurrent neural networks (RNNs) and LSTMs handle sequences. Transformer architecture underlies modern NLP — know attention mechanisms conceptually even if you don't implement them from scratch.
Model evaluation metrics depend on the task. For regression: MAE, MSE, RMSE, and R². For classification: accuracy (misleading with imbalance), precision, recall, F1, and AUC-ROC. For ranking: MAP, NDCG. Cross-validation strategies include k-fold, stratified k-fold for imbalanced classes, and time-series split for temporal data.
A/B testing requires understanding statistical power, sample size calculation, and experiment duration. Common pitfalls include peeking at results early (p-hacking), novelty effects, and Simpson's paradox. Know how to segment results and handle network effects in social platforms.
For large-scale data, Apache Spark is the industry standard. Understand the RDD vs. DataFrame API, lazy evaluation, transformations vs. actions, and partitioning. PySpark is the Python interface — practice reading from S3/HDFS, running SQL via spark.sql(), and caching intermediate results.
Hadoop's HDFS and MapReduce provide the storage and processing foundation, though most modern pipelines use Spark on top of cloud object storage. Know the ecosystem: Hive for SQL-on-Hadoop, HBase for NoSQL, and Kafka for streaming data ingestion.
Print the PDF and work through it as a timed practice session — simulate real exam conditions by setting a timer and avoiding outside resources. After completing it, review every answer carefully, especially the ones you got right by guessing. Understanding why an answer is correct reinforces conceptual understanding more than memorizing the answer itself.
Use the checklist above to identify weak areas, then return to those topics before your next practice session. For interactive practice, unlimited question banks, and instant scoring, visit our Data Science practice tests — they cover every domain in this PDF with detailed explanations for each question.