SnowPro Advanced: Data Scientist Preparation Details
The SnowPro Advanced: Data Scientist (DSA-C03) exam validates advanced data science and machine learning skills using Snowflake. Candidates are tested on data preparation, feature engineering, model development, and deployment across Snowflake’s Data Cloud. This guide maps every domain and objective to official Snowflake documentation. You can also explore more Snowflake certification study guides on the Snowflake Certification category to keep building your skills.
SnowPro Advanced: Data Scientist Materials
Domain 1.0: Data Science Concepts
1.1 Define machine learning concepts for data science workloads.
Machine Learning: Supervised learning
Classification (Snowflake ML Functions)
Machine Learning: Unsupervised learning
snowflake.ml.modeling.cluster.KMeans
Snowflake ML Model Development
Machine Learning: Reinforcement learning
1.2 Identify machine learning problem types.
Supervised Learning: Structured Data: Linear regression
Supervised Learning: Structured Data: Binary classification
Classification (Snowflake ML Functions)
snowflake.ml.modeling.metrics.accuracy_score
Supervised Learning: Structured Data: Multi-class classification
Classification (Snowflake ML Functions)
snowflake.ml.modeling.metrics.f1_score
snowflake.ml.modeling.linear_model.LogisticRegressionCV
Supervised Learning: Structured Data: Time-series forecasting
Time-Series Forecasting (Snowflake ML Functions)
Dealing with real-world data in Time-Series Forecasting
Supervised Learning: Unstructured Data: Image classification
Snowflake Cortex AISQL (including LLM functions)
Supervised Learning: Unstructured Data: Segmentation
Snowflake ML Model Development
Unsupervised Learning: Clustering
snowflake.ml.modeling.cluster.KMeans
Snowflake ML Model Development
GenAI: Association models
Snowflake Cortex AISQL (including LLM functions)
Fine-tuning (Snowflake Cortex)
1.3 Summarize the machine learning lifecycle.
Data collection
Introduction to external tables
Data visualization and exploration
Querying data using worksheets
Feature engineering
Snowflake ML Model Development
Training models
Snowflake ML Model Development
Model deployment
Model monitoring and evaluation (e.g., model explainability, precision, recall, accuracy, confusion matrix)
snowflake.ml.modeling.metrics.accuracy_score
snowflake.ml.modeling.metrics.f1_score
Model explainability visualizations
Model versioning
Snowflake ML Model Development
1.4 Define statistical concepts for data science.
Normal versus skewed distributions (e.g., mean, outliers)
Anomaly Detection (Snowflake ML Functions)
Central limit theorem
Z and T tests
Bootstrapping
snowflake.ml.modeling.model_selection.GridSearchCV
Confidence intervals
Time-Series Forecasting (Snowflake ML Functions)
Anomaly Detection (Snowflake ML Functions)
Domain 2.0: Data Preparation and Feature Engineering
2.1 Prepare and clean data in Snowflake.
Use Snowpark for Python and SQL: Aggregate
Writing Snowpark Code in Python
Working with DataFrames in Snowpark Python
Use Snowpark for Python and SQL: Joins
Working with DataFrames in Snowpark Python
Writing Snowpark Code in Python
Use Snowpark for Python and SQL: Identify critical data
Writing Snowpark Code in Python
Working with DataFrames in Snowpark Python
Use Snowpark for Python and SQL: Remove duplicates
Working with DataFrames in Snowpark Python
Writing Snowpark Code in Python
Use Snowpark for Python and SQL: Remove irrelevant fields
Working with DataFrames in Snowpark Python
Writing Snowpark Code in Python
Use Snowpark for Python and SQL: Handle missing values
Working with DataFrames in Snowpark Python
Use Snowpark for Python and SQL: Data type casting
Working with DataFrames in Snowpark Python
Use Snowpark for Python and SQL: Sampling data
Working with DataFrames in Snowpark Python
Writing Snowpark Code in Python
2.2 Perform exploratory data analysis in Snowflake.
Snowpark and SQL: Identify initial patterns (i.e., data profiling)
Writing Snowpark Code in Python
Snowpark and SQL: Connect external machine learning platforms and/or notebooks (e.g., Jupyter)
Import Python packages to use in notebooks
Use Snowflake native statistical functions to analyze and calculate descriptive data statistics.: Window Functions
Use Snowflake native statistical functions to analyze and calculate descriptive data statistics.: MIN/MAX/AVG/STDEV
Use Snowflake native statistical functions to analyze and calculate descriptive data statistics.: VARIANCE
Use Snowflake native statistical functions to analyze and calculate descriptive data statistics.: TOPn
Use Snowflake native statistical functions to analyze and calculate descriptive data statistics.: Approximation/High Performing function
Linear Regression: Find the slope and intercept
Linear Regression: Verify the dependencies on dependent and independent variables
2.3 Perform feature engineering on Snowflake data.
Preprocessing: Scaling data
snowflake.ml.modeling.preprocessing.StandardScaler
snowflake.ml.modeling.preprocessing.MinMaxScaler
Preprocessing: Encoding
snowflake.ml.modeling.preprocessing.OneHotEncoder
Preprocessing: Normalization
snowflake.ml.modeling.preprocessing.StandardScaler
Data Transformations: DataFrames (i.e., pandas, Snowpark, Snowpark pandas)
Working with DataFrames in Snowpark Python
Writing Snowpark Code in Python
Data Transformations: Derived features (e.g., average spend)
Binarizing data: Binning continuous data into intervals
Binarizing data: Label encoding
snowflake.ml.modeling.preprocessing.OneHotEncoder
Binarizing data: One hot encoding
snowflake.ml.modeling.preprocessing.OneHotEncoder
Snowpark Feature Store
Creating or connecting to a feature store
snowflake.ml.feature_store.FeatureStore
2.4 Visualize and interpret the data to present a business case.
Statistical summaries: Snowsight with SQL
Querying data using worksheets
Statistical summaries: Interpret open-source graph libraries
Import Python packages to use in notebooks
Statistical summaries: Identify data outliers
Anomaly Detection (Snowflake ML Functions)
Snowflake Notebooks
Schedule your Snowflake Notebook to run
Import Python packages to use in notebooks
Domain 3.0: Model Development
3.1 Connect data science tools directly to data in Snowflake.
Connecting Python to Snowflake: Snowpark
Writing Snowpark Code in Python
Working with DataFrames in Snowpark Python
Connecting Python to Snowflake: Snowpark ML
Snowflake ML Model Development
Connecting Python to Snowflake: Python connector with Pandas support
Using pandas DataFrames with the Python Connector
Connecting from external IDE (e.g., Visual Studio Code)
Snowflake Extension for Visual Studio Code
Writing Snowpark Code in Python
Snowpark languages
Writing Snowpark Code in Python
User-defined functions overview
3.2 Leverage GenAI and LLM models in Snowflake.
Snowflake Cortex: Vector embedding
Snowflake Cortex AISQL (including LLM functions)
Snowflake Cortex: Prompt engineering
Snowflake Cortex AISQL (including LLM functions)
Fine-tuning (Snowflake Cortex)
Snowflake Cortex: Fine tuning
Fine-tuning (Snowflake Cortex)
FINETUNE (‘CREATE’) (SNOWFLAKE.CORTEX)
Snowflake Cortex AISQL (including LLM functions)
Snowflake Cortex: Task-specific models (e.g., categorization, summarization, sentiment analysis, information extraction)
Snowflake Cortex AISQL (including LLM functions)
Fine-tuning (Snowflake Cortex)
3.3 Train a data science model.
Build a data science pipeline: Automation of data transformation (e.g., dynamic tables)
Build a data science pipeline: Python User-Defined Functions (UDFs)
User-defined functions overview
Creating User-Defined Functions (UDFs) for DataFrames in Python
Build a data science pipeline: Python User-Defined Table Functions (UDTFs)
Creating User-Defined Table Functions (UDTFs) for DataFrames in Python
User-defined functions overview
Build a data science pipeline: Python stored procedures
Choosing whether to write a stored procedure or a user-defined function
Hyperparameter tuning
snowflake.ml.modeling.model_selection.GridSearchCV
snowflake.ml.modeling.model_selection.RandomizedSearchCV
Optimization metric selection (e.g., log loss, AUC, RMSE)
snowflake.ml.modeling.metrics.log_loss
snowflake.ml.modeling.metrics.roc_auc_score
Classification (Snowflake ML Functions)
Partitioning: Cross validation
snowflake.ml.modeling.model_selection.GridSearchCV
snowflake.ml.modeling.linear_model.LogisticRegressionCV
Partitioning: Train validation hold-out
snowflake.ml.modeling.model_selection.GridSearchCV
Snowflake ML Model Development
Down/up-sampling
Training with Python stored procedures
Choosing whether to write a stored procedure or a user-defined function
Training outside Snowflake through external functions
Writing Snowpark Code in Python
Training with Python User-Defined Table Functions (UDTFs)
Creating User-Defined Table Functions (UDTFs) for DataFrames in Python
3.4 Validate a data science model.
ROC curve/confusion matrix: Calculate the expected payout of the model
snowflake.ml.modeling.metrics.roc_auc_score
Classification (Snowflake ML Functions)
snowflake.ml.modeling.metrics.f1_score
Regression problems
Residuals plot: Interpret graphics with context
Model explainability visualizations
Classification (Snowflake ML Functions)
Model metrics
snowflake.ml.modeling.metrics.accuracy_score
snowflake.ml.modeling.metrics.f1_score
snowflake.ml.modeling.metrics.log_loss
3.5 Interpret a model.
Feature impact
Model explainability visualizations
snowflake.ml.registry.Registry
Partial dependence plots
Model explainability visualizations
Confidence intervals
Time-Series Forecasting (Snowflake ML Functions)
Anomaly Detection (Snowflake ML Functions)
SHAP values
Model explainability visualizations
snowflake.ml.registry.Registry
Domain 4.0: Model Deployment
4.1 Move a data science model into production.
Use an external hosted model: External functions
Use an external hosted model: Pre-built models
Snowflake Cortex AISQL (including LLM functions)
Deploy a model in Snowflake: Vectorized/Scalar Python User-Defined Functions (UDFs)
User-defined functions overview
Deploy a model in Snowflake: Pre-built models
Snowflake Cortex AISQL (including LLM functions)
Deploy a model in Snowflake: Storing predictions
snowflake.ml.registry.Registry
Deploy a model in Snowflake: Stage commands
Deploy a model in Snowflake: Snowflake Model Registry: Model logging and retrieving
snowflake.ml.registry.Registry
Deploy a model in Snowflake: Snowflake Model Registry: Snowpark Container Services
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?)
Anomaly Detection (Snowflake ML Functions)
Classification (Snowflake ML Functions)
Area under the curve
snowflake.ml.modeling.metrics.roc_auc_score
Classification (Snowflake ML Functions)
Accuracy, precision, recall
snowflake.ml.modeling.metrics.accuracy_score
snowflake.ml.modeling.metrics.f1_score
Classification (Snowflake ML Functions)
RMSE (regression)
4.3 Outline model lifecycle and validation tools.
Metadata tagging
snowflake.ml.registry.Registry
Model versioning with Snowflake Model Registry
snowflake.ml.registry.Registry
Automation of model retraining
Wrapping Up SnowPro Advanced: Data Scientist
This guide covered every domain of the SnowPro Advanced: Data Scientist (DSA-C03) exam, from data preparation and feature engineering to model development and deployment in Snowflake. With official documentation linked for each objective, you have a solid foundation for hands-on practice before exam day. Keep working through the Snowflake ML and Cortex features covered here to build real confidence. You can also explore more Snowflake certification study guides on the Snowflake Certification category to keep building your skills. Have a question or tip? Leave a comment below.
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