SNOWFLAKESnowflake SnowPro Advanced — Data Scientist
The SnowPro Advanced: Data Scientist certification validates your ability to apply comprehensive data science principles, tools, and methodologies using Snowflake. Designed for experienced data scientists, it covers data science concepts, feature engineering, machine learning, and GenAI/LLM capabilities. Earning this credential demonstrates advanced, production-ready expertise in the Snowflake AI Data Cloud.
333 practice questions · Updated 2026-07-30
5Domains
14Objectives
109Concepts
333Questions
SNOWPRO-ADVANCED-DATA-SCIENTIST Curriculum
Every domain, objective, and concept the SNOWPRO-ADVANCED-DATA-SCIENTIST exam measures.
- Machine Learning Fundamentals
- Model Training and Evaluation
- Feature Engineering
- Overfitting and Underfitting
- Bias-Variance Tradeoff
- Common ML Algorithms
- Supervised learning
- Unsupervised learning
- Reinforcement learning
- Distinguishing learning paradigms
- Classification
- Regression
- Clustering
- Anomaly Detection
- Recommendation Systems
- Time Series Forecasting
- Dimensionality Reduction
- Association Rule Learning
- Data pipeline design for ML
- Data ingestion strategies
- Data transformation and cleaning
- Feature engineering in pipelines
- Handling missing and anomalous data
- Data versioning and reproducibility
- Pipeline automation and orchestration
- Performance optimization for pipelines
- Integration with ML frameworks
- Monitoring and logging pipelines
- Data Ingestion Methods
- File Formats and Staging
- COPY INTO Command
- Snowpipe for Continuous Ingestion
- Data Transformation During Load
- Data Preparation and Cleaning
- Semi-Structured Data Handling
- Data Quality and Validation
- Error Handling and Monitoring
- Data Cleaning Fundamentals
- Handling Missing Values
- Outlier Detection and Treatment
- Data Type Conversion
- Deduplication
- Standardizing and Normalizing Data
- Handling Categorical Data
- Text Data Cleaning
- Date and Time Parsing
- Data Validation and Quality Checks
- Feature engineering overview
- Feature creation
- Feature selection
- Feature transformation
- Handling missing values
- Handling categorical variables
- Handling numerical variables
- Feature extraction from text
- Feature extraction from time series
- Feature interactions
- Dimensionality reduction
- Feature importance evaluation
- Automated feature engineering
- Feature engineering for specific model types
- Feature engineering pipeline
- Identify missing data patterns
- Apply imputation techniques
- Handle categorical variables
- Manage high-cardinality categories
- Snowpark ML Modeling API
- Model Training Workflow
- Integration with Snowflake Data
- Hyperparameter Tuning
- Model Persistence and Deployment
- Define model performance metrics
- Select appropriate evaluation metrics
- Interpret evaluation results
- Compare model performance
- Validate model performance
- Hyperparameter tuning overview
- Common hyperparameters
- Grid search
- Random search
- Bayesian optimization
- Cross-validation for tuning
- Evaluation metrics for tuning
- Overfitting and underfitting in tuning
- Automated tuning tools
- Resource constraints
- Model deployment options in Snowflake
- Creating and using Python UDFs for model inference
- Leveraging Snowpark for model deployment
- Model versioning and management
- Integration with external model serving platforms
- Performance optimization for model inference
- Security and access control for deployed models
- Monitoring and logging model predictions
- Model monitoring metrics
- Data drift detection
- Model retraining strategies
- Alerting and incident response
- Model versioning and rollback
- Operational dashboards
- Model Registry Overview
- Registering Models
- Model Versioning
- Model Metadata Management
- Model Discovery and Search
- Model Deployment
- Model Inference and Predictions
- Model Lifecycle Management
- Integration with Snowflake ML
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Percentages reflect share of the current practice bank, not official exam weightings — no structured per-skill weight is published for SNOWPRO-ADVANCED-DATA-SCIENTIST, so none is invented.