Snowflake DSA-C02 Exam Details & Actual Exam Questions

  • Exam Code/Number: DSA-C02
  • Exam Name/Title: SnowPro Advanced: Data Scientist Certification Exam
  • Certification Provider: Snowflake
  • Corresponding Certification: SnowPro Advanced Certification
  • Exam Questions: 67
  • Updated On: Jul,20 2026
  • Certification Level: Advanced

Snowflake SnowPro Advanced: Data Scientist Certification Exam Questions

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Snowflake DSA-C02 Exam Overview:

Certification Vendor:Snowflake
Exam Name:SnowPro Advanced: Data Scientist Certification Exam
Exam Number:DSA-C02
Certificate Validity Period:2 years
Exam Format:Multiple Choice, Multiple Select, Scenario-based Questions
Available Languages:English
Passing Score:750/1000
Related Certifications:SnowPro Core Certification
SnowPro Advanced: Data Scientist
Exam Duration:115 minutes
Exam Price:$375 USD
Real Exam Qty:65
Sample Questions:Snowflake DSA-C02 Sample Questions
Exam Way:Online proctored exam or test center delivery through Pearson VUE
Pre Condition:Active SnowPro Core Certification is recommended. Snowflake recommends 2+ years of hands-on experience as a Data Scientist using Snowflake in a production environment.
Official Syllabus URL:https://learn.snowflake.com/en/certifications/snowpro-advanced-datascientistC03

Snowflake DSA-C02 Exam Syllabus Topics:

SectionWeightObjectives
Model Deployment and Operations20-25%- Monitoring and Governance
  • 1. Manage model versions
  • 2. Ensure security and compliance
  • 3. Monitor model performance
- Model Deployment
  • 1. Batch and real-time inference
  • 2. Deploy models in Snowflake
  • 3. Production integration patterns
- Generative AI and Advanced Capabilities
  • 1. Evaluate AI model outputs
  • 2. Apply GenAI workflows
  • 3. Use LLM capabilities in Snowflake
Data Science Concepts15-20%- Machine Learning Fundamentals
  • 1. Unsupervised learning
  • 2. Supervised learning
  • 3. Classification and regression
  • 4. Model evaluation metrics
- Data Science Methodologies
  • 1. Experiment design
  • 2. Bias and variance concepts
  • 3. Feature selection techniques
  • 4. Cross-validation methods
Data Preparation and Feature Engineering25-30%- Feature Engineering
  • 1. Normalization and scaling
  • 2. Create derived features
  • 3. Use Snowpark DataFrames
  • 4. Encoding categorical variables
- Data Preparation
  • 1. Sampling and aggregation
  • 2. Clean and transform data in Snowflake
  • 3. Data type conversion
  • 4. Handle missing values
- Exploratory Data Analysis
  • 1. Analyze distributions
  • 2. Identify correlations
  • 3. Detect anomalies
Model Development30-35%- Snowflake Machine Learning Capabilities
  • 1. Integrated machine learning services
  • 2. Python-based data science development
  • 3. Snowpark ML workflows
- Model Evaluation
  • 1. Interpret model results
  • 2. Compare candidate models
  • 3. Evaluate model performance
- Model Training
  • 1. Train machine learning models
  • 2. Model selection strategies
  • 3. Hyperparameter tuning


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