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.
Exam experiencesNew
Pass and fail outcomes from candidates who prepared here — advice, scores, and prep time.
Start Practicing
All Domains
Practice with randomly mixed questions from all topics
Domain Mode
Practice questions from a specific topic area
Quiz History
Exam Details
Key information about Snowflake SnowPro Advanced: Data Scientist (DSA-C03)
- Multiple choice
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 - GenAI - Association models
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
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