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.
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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
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