SnowPro Advanced: Data Scientist

The SnowPro Advanced: Data Scientist certification validates expert-level ability to apply comprehensive data science principles, tools, and methodologies using Snowflake. It covers the full data science lifecycle — from data preparation and feature engineering to training machine learning models with Snowpark, deploying ML solutions, and leveraging Snowflake's GenAI and LLM capabilities. Candidates must demonstrate 2+ years of hands-on data science experience in a Snowflake production environment.

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What is the SnowPro Advanced: Data Scientist Certification?

Who Should Take This Course?

Data Scientists with 2+ years of Snowflake production experience
ML Engineers building end-to-end ML pipelines on Snowflake
AI/ML Architects designing Snowflake-native ML solutions
Data Scientists leveraging Snowpark for Python-based ML workflows
Professionals building GenAI and LLM applications in Snowflake
Senior data practitioners seeking advanced Snowflake credentialing

What You Will Learn in the DSA-C01 Course

A comprehensive curriculum covering all exam objectives with hands-on labs and real-world practice.

Data Science Concepts and Best Practices

Apply foundational and advanced data science principles within Snowflake.

Statistical analysis and exploratory data analysis (EDA)
Supervised vs. unsupervised learning approaches
Snowflake data science best practices and architectural patterns
Experiment design and hypothesis testing

Data Preparation and Feature Engineering

Transform raw data into model-ready features inside Snowflake.

Data cleaning, normalization, and imputation techniques
Feature selection and dimensionality reduction
Feature Store usage and feature pipelines
Handling time-series and semi-structured data for ML

Machine Learning with Snowpark

Train, evaluate, and tune ML models using Snowpark's Python DataFrame API.

Snowpark for Python: DataFrames and UDFs for ML
Training scikit-learn and XGBoost models via Snowpark
Snowpark ML: preprocessing, model training, and evaluation
Hyperparameter tuning and cross-validation strategies

Model Deployment and MLOps

Deploy models and operationalize ML workflows in Snowflake.

Deploying models as Snowpark User-Defined Functions
Model Registry for versioning and lifecycle management
Batch inference using Snowpark stored procedures
Model monitoring, drift detection, and retraining pipelines

GenAI and LLM Capabilities

Build generative AI and LLM-powered applications on Snowflake.

Cortex AI: built-in LLM functions (COMPLETE, SUMMARIZE, CLASSIFY)
Retrieval-Augmented Generation (RAG) with Cortex Search
Fine-tuning and prompt engineering in Snowflake
Snowflake Arctic and external model integration

Course Prerequisites

Pre-requisites training is free when you purchase the course from ProSupport

SnowPro Core Certification (COF-C02) — mandatory prerequisite
2+ years of hands-on experience as a Data Scientist using Snowflake in production
Proficiency in Python, SQL, and data science libraries (scikit-learn, pandas, NumPy)
Experience with R, PySpark, or other data science languages is advantageous
Familiarity with ML model lifecycle, including training, evaluation, and deployment

Exam Information

Everything you need to know about the DSA-C01 certification exam.

Exam Component	Details
Exam Name	SnowPro Advanced: Data Scientist
Exam Code	DSA-C01
Exam Type	Multiple Choice and Multiple Select
Total Questions	65
Passing Score	750 (out of 1000)
Exam Duration	115 minutes
Language	English
Exam Provider	Snowflake / Kryterion (online proctored or test center)
Exam Focus	Data science best practices, feature engineering, Snowpark ML, model deployment, and GenAI on Snowflake
Exam Registration	Snowflake certification portal (learn.snowflake.com)
Retake Policy	No mandatory waiting period between attempts
Certification Validity	2 years