SnowPro Advanced: Data Scientist

DSA-C03 · ● Active · Professional · Snowflake

Advanced Snowflake certification validating expertise in machine learning, Snowpark ML, Feature Store, Model Registry, and Cortex AI for production data science workloads on the Snowflake Data Cloud.

Exam facts

Vendor source — Snowflake Certifications ↗

Official exam page — SnowPro Advanced: Data Scientist ↗

FAQ & details — SnowPro Data Scientist FAQs ↗

Exam blueprint — DSA-C03 Study Guide ↗

About

SnowPro Advanced: Data Scientist (DSA-C03) is Snowflake's role-based professional certification for data scientists and ML engineers applying advanced machine learning techniques natively within Snowflake. Released in 2024, it supersedes earlier iterations and validates hands-on proficiency with Snowpark ML, the integrated Feature Store, Model Registry for MLOps, Cortex AI for generative AI tasks, and Python-based development. Candidates need an active SnowPro Core cert and at least two years of production Snowflake experience. The exam emphasizes building, training, validating, and deploying real-world ML models directly on Snowflake's platform.

Domain context — Data & AI

Hyperscale cloud data platforms with integrated ML and AI capabilities. Snowflake's unified architecture combines data warehousing, data lakes, and machine learning in a single SaaS platform.

Read full deep dive — Snowflake Data Cloud Ecosystem →

Topics covered

Domain 1: Machine Learning Concepts (25%)

• Defining ML concepts for data science workloads (supervised, unsupervised, reinforcement learning)
• ML problem identification (linear regression, binary/multi-class classification, time-series forecasting, image classification, segmentation)
• Feature engineering and selection for model performance

Domain 2: Data Preparation & Exploratory Analysis (25%)

• Data cleaning, transformation, and validation
• Exploratory Data Analysis (EDA) with Snowflake and Notebooks
• Data pipeline design with Snowpark

Domain 3: Model Building, Training & Optimization (30%)

• Snowpark ML APIs for end-to-end model development
• Hyperparameter tuning and optimization
• Cross-validation techniques
• Handling imbalanced datasets and class weighting
• Leveraging Cortex AI and LLM-based features
• Model versioning and experiment tracking

Domain 4: Model Validation, Deployment & Governance (20%)

• Model evaluation metrics (ROC curves, confusion matrices, precision, recall, F1-score)
• Production deployment via Model Registry
• Batch and real-time inference pipelines
• Model governance, monitoring, and performance tracking
• Feature Store integration for consistent feature serving

Source: VMExam DSA-C03 Syllabus ↗

Common skills at Data & AI · Professional

• Python (pandas, numpy, scikit-learn, PyTorch, TensorFlow integration)
• SQL for data transformation and feature creation
• Statistical analysis and hypothesis testing
• Machine learning fundamentals (model selection, evaluation, tuning)
• Cloud data platform architecture and optimization
• MLOps and model lifecycle management
• Big data processing and distributed computing concepts
• Data visualization and storytelling with analytics
• API development and integration
• DevOps basics (CI/CD for ML pipelines)

Recommended courses at Data & AI · Professional

Practice exams

Books

Typical job titles at Data & AI · Professional

ML Engineer · Senior Data Scientist · Machine Learning Specialist · Data Science Engineer · Cortex AI Developer · Snowpark ML Architect · MLOps Engineer

(Job titles drawn from Snowflake careers and Indeed.com postings listing Snowflake ML expertise as required or preferred.)

Salary

Note: Salary ranges represent data scientist roles in cloud data platforms; Snowflake-certified roles often command premium pay (10–20% above median).

Exchange rate as of May 1, 2026: 1 USD = 16.67 ZAR

Skills validated

• Snowpark ML APIs (Python-based ML development)
• Feature Store design, versioning, and serving
• Model Registry for MLOps and governance
• Snowflake Cortex AI (LLM functions, embeddings, fine-tuning)
• Snowflake Notebooks and IDE integration (Jupyter, VS Code)
• Python for data science (pandas, numpy, scikit-learn, PyTorch, TensorFlow integration)
• SQL for feature engineering and data transformation
• Hyperparameter tuning and cross-validation
• Model evaluation (ROC, AUC, confusion matrices, F1-score)
• Production deployment and batch/real-time inference
• Data pipeline orchestration with Snowflake Tasks
• Model monitoring and performance tracking

Related certifications

• Prerequisite: SnowPro Core (COF-C03) ↗
• Stacks with: SnowPro Advanced: Data Engineer (DEA-C02) ↗
• Vendor overview: Snowflake Vendor Overview ↗

Sources

• Snowflake Certifications ↗
• SnowPro Advanced: Data Scientist Official Page ↗
• SnowPro Data Scientist FAQs ↗
• VMExam DSA-C03 Exam Syllabus ↗
• Snowflake ML Documentation ↗
• Snowflake Cortex AI Functions ↗
• USD to ZAR Exchange Rates ↗
• Glassdoor Data Scientist Salary (US) ↗
• PayScale Data Scientist ZA ↗

Last verified: 2026-05-01

Parent ecosystem: Snowflake Data Cloud Ecosystem

Domain: Data & AI

Vendor: Snowflake