This DP-100 Practice Test Data Science: 1500 Certified Questions course is designed to give you a clear, structured path through data exploration, feature engineering, model training, evaluation, deployment and responsible AI practices. The questions are written around realistic data science and ML project scenarios, so you connect techniques with how they are applied in practice.

The course contains 1,500 questions divided into six sections of 250 questions each, giving you repeated exposure to core data science decisions from problem framing to production.

You begin with Data Science Process, Problem Framing & Data Acquisition — 250 Questions, where you learn to translate business problems into measurable data science tasks and understand what data is required to support them.

The second section, Data Exploration, Profiling & Data Quality Assessment — 250 Questions, focuses on exploratory data analysis, including distributions, correlations, missing values, outliers and class imbalance, helping you read datasets critically before modeling.

In the third section, Feature Engineering, Data Preparation & Transformation Pipelines — 250 Questions, you practice building robust data preparation flows, handling encoding, scaling, text and time features while avoiding data leakage and ensuring reproducibility.

The fourth section, Model Training, Selection & Optimization Techniques — 250 Questions, develops your ability to choose and tune models systematically, using validation strategies, hyperparameter search and bias-variance reasoning instead of guesswork.

The fifth section, Model Evaluation, Validation, Monitoring & MLOps Foundations — 250 Questions, shows you how to evaluate models with appropriate metrics, design fair comparisons and monitor performance over time using MLOps-oriented thinking.

Finally, the sixth section, Deployment, Governance & Responsible AI Practices — 250 Questions, connects deployment with governance, fairness, explainability, privacy and long-term accountability, so you treat production models as ongoing responsibilities.

Each practice test can be retaken as many times as you need, helping you track and enhance your progress, strengthen weaker areas and build structured confidence. Whether you are preparing for DP-100-style objectives or working on real data science projects, this course offers a section-based path to practical, modern data science skills.