Free Practice Questions for Snowflake DSA-C03 Certification
Study with 479 exam-style practice questions designed to help you prepare for the Snowflake SnowPro Advanced: Data Scientist (DSA-C03). All questions are aligned with the latest exam guide and include detailed explanations to help you master the material.
Start Practicing
Random Questions
Practice with randomly mixed questions from all topics
Domain Mode
Practice questions from a specific topic area
Exam Information
Exam Details
Key information about Snowflake SnowPro Advanced: Data Scientist (DSA-C03)
associate (intermediate)
Snowflake Continuing Education (CE) program (eligible ILT Training Courses, equivalent or higher-level SnowPro Certification)
January 12, 2026
Active SnowPro Core Certified credential
2+ years of practical data science experience with Snowflake in an enterprise environment; Data Scientists, AI or ML Engineers
10 – 13 hours
2 years
Exam Topics & Skills Assessed
Skills measured (from the official study guide)
Domain 1: Data Science Concepts
Subdomain 1.1: Define machine learning concepts for data science workloads.
- Machine Learning - Supervised learning - Unsupervised learning - Reinforcement learning
Subdomain 1.2: Identify machine learning problem types.
- Supervised Learning - Structured Data - Linear regression - Binary classification - Multi-class classification - Time-series forecasting - Unstructured Data - Image classification - Segmentation - Unsupervised Learning - Clustering
Subdomain 1.3: Summarize the machine learning lifecycle.
- Data collection - Data visualization and exploration - Feature engineering - Training models - Model deployment - Model monitoring and evaluation (e.g., model explainability, precision, recall, accuracy, confusion matrix) - Model versioning
Subdomain 1.4: Define statistical concepts for data science.
- Normal versus skewed distributions (e.g., mean, outliers) - Central limit theorem - Z and T tests - Bootstrapping - Confidence intervals - GenAI - Association models
Domain 2: Data Preparation and Feature Engineering
Subdomain 2.1: Prepare and clean data in Snowflake.
- Use Snowpark for Python and SQL - Aggregate - Joins - Identify critical data - Remove duplicates - Remove irrelevant fields - Handle missing values - Data type casting - Sampling data
Subdomain 2.2: Perform exploratory data analysis in Snowflake.
- Snowpark and SQL - Identify initial patterns (i.e., data profiling) - Connect external machine learning platforms and/or notebooks (e.g., Jupyter)
- Use Snowflake native statistical functions to analyze and calculate descriptive data statistics. - Window Functions - MIN/MAX/AVG/STDEV - VARIANCE - TOPn - Approximation/High Performing function
- Linear Regression - Find the slope and intercept - Verify the dependencies on dependent and independent variables
Subdomain 2.3: Perform feature engineering on Snowflake data.
- Preprocessing - Scaling data - Encoding - Normalization - Data Transformations - DataFrames (i.e., pandas, Snowpark, Snowpark pandas) - Derived features (e.g., average spend) - Binarizing data - Binning continuous data into intervals - Label encoding - One hot encoding - Snowpark Feature Store
Subdomain 2.4: Visualize and interpret the data to present a business case.
- Statistical summaries - Snowsight with SQL - Interpret open-source graph libraries - Identify data outliers - Snowflake Notebooks
Domain 3: Model Development
Subdomain 3.1: Connect data science tools directly to data in Snowflake.
- Connecting Python to Snowflake - Snowpark - Snowpark ML - Python connector with Pandas support - Connecting from external IDE (e.g., Visual Studio Code) - Snowpark languages
Subdomain 3.2: Leverage GenAI and LLM models in Snowflake.
- Snowflake Cortex - Vector embedding - Prompt engineering - Fine tuning - Task-specific models (e.g., categorization, summarization, sentiment analysis, information extraction)
Subdomain 3.3: Train a data science model.
- Build a data science pipeline - Automation of data transformation (e.g., dynamic tables) - Python User-Defined Functions (UDFs) - Python User-Defined Table Functions (UDTFs) - Hyperparameter tuning - Optimization metric selection (e.g., log loss, AUC, RMSE) - Partitioning - Cross validation - Train validation hold-out - Down/up-sampling - Training with Python stored procedures - Training outside Snowflake through external functions - Training with Python User-Defined Table Functions (UDTFs)
Subdomain 3.4: Validate a data science model.
- ROC curve/confusion matrix - Calculate the expected payout of the model - Regression problems - Residuals plot - Interpret graphics with context - Model metrics
Subdomain 3.5: Interpret a model.
- Feature impact - Partial dependence plots - Confidence intervals - SHAP values - Python stored procedures
Domain 4: Model Deployment
Subdomain 4.1: Move a data science model into production.
- Use an external hosted model - External functions - Pre-built models - Deploy a model in Snowflake - Vectorized/Scalar Python User-Defined Functions (UDFs) - Pre-built models - Storing predictions - Stage commands - Snowflake Model Registry - Model logging and retrieving - Snowpark Container Services
Subdomain 4.2: Determine the effectiveness of a model and retrain if necessary.
- Metrics for model evaluation - Data drift /Model decay - Data distribution comparisons (Do the data making predictions look similar to the training data? Do the same data points give the same predictions once a model is deployed?) - Area under the curve - Accuracy, precision, recall - RMSE (regression)
Subdomain 4.3: Outline model lifecycle and validation tools.
- Metadata tagging - Model versioning with Snowflake Model Registry - Automation of model retraining
Techniques & products