SnowPro® Advanced: Data Scientist (DSA-C03) Exam Questions

About this course

About the Certification: SnowPro® Advanced: Data Scientist (DSA-C03)The SnowPro® Advanced: Data Scientist (DSA-C03) certification focuses on the skills needed to build, evaluate, deploy, and manage machine learning solutions within Snowflake in real business environments.Candidates preparing for this certification are expected to understand how Snowflake supports the complete data science workflow, including data preparation, feature engineering, model development, evaluation, deployment, and monitoring.A major focus area is data preparation and feature engineering. Learners should understand how to clean and transform datasets using Snowpark and SQL, including joins, aggregations, handling missing values, removing duplicates, encoding variables, scaling data, and creating derived features for modeling.The certification also covers exploratory data analysis and statistical techniques. Candidates should know how to analyze data distributions, identify outliers, apply descriptive statistics, and use Snowflake functions such as window functions, variance, and approximation methods to uncover patterns in data.Another key area is model development and machine learning problem solving. Learners are expected to understand supervised and unsupervised learning, including classification, regression, clustering, and forecasting, along with model training approaches, hyperparameter tuning, and validation strategies such as cross-validation and hold-out methods.A critical domain is model evaluation and interpretation. Candidates should be familiar with metrics such as accuracy, precision, recall, AUC, and RMSE, and be able to interpret confusion matrices, ROC curves, and residual plots. They should also understand feature impact techniques such as SHAP values and partial dependence plots.The certification also includes model deployment and integratio

Course objectives

• prepare for the SnowPro® Advanced Data Scientist certification
• acquire skills in machine learning model development and evaluation
• learn to clean and transform datasets effectively