Snowflake DSA-C03 Exam Details & Actual Exam Questions

  • Exam Code/Number: DSA-C03
  • Exam Name/Title: SnowPro Advanced: Data Scientist Certification Exam
  • Certification Provider: Snowflake
  • Corresponding Certification: SnowPro Advanced
  • Exam Questions: 289
  • Updated On: Jul,25 2026
  • Certification Level: Advanced

Snowflake SnowPro Advanced: Data Scientist Certification Exam Questions

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Snowflake DSA-C03 Exam Overview:

Certification Vendor:Snowflake
Exam Name:SnowPro Advanced: Data Scientist Certification Exam
Exam Number:DSA-C03
Available Languages:English, Japanese, Simplified Chinese
Exam Duration:115 minutes
Exam Price:375 USD
Exam Format:Multiple choice, Multiple select
Passing Score:750 (scaled score 0–1000)
Related Certifications:SnowPro Core
SnowPro Advanced: Data Engineer
SnowPro Advanced: Architect
Certificate Validity Period:2 years
Real Exam Qty:65
Recommended Training:DSA-C03 Study Guide
Snowflake Official Training
Exam Registration:Pearson VUE Registration
Sample Questions:Snowflake DSA-C03 Sample Questions
Exam Way:Online proctored or onsite test center via Pearson VUE
Pre Condition:2+ years hands-on experience with Snowflake as Data Scientist; proficiency in SQL, Python, or similar languages recommended
Official Syllabus URL:https://learn.snowflake.com/en/certifications/snowpro-advanced-datascientistC03/

Snowflake DSA-C03 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Machine Learning Model Development and Training25%- Training and optimization
  • 1. Hyperparameter tuning
  • 2. Model validation and testing
  • 3. Using Snowflake ML and Snowpark
- Model types and selection
  • 1. Supervised learning
  • 2. Unsupervised learning
  • 3. Time-series models
Topic 2: Generative AI and LLM Capabilities15%- Generative AI use cases
  • 1. Retrieval-augmented generation
  • 2. Text generation and summarization
- LLM integration in Snowflake
  • 1. Embeddings and vector search
  • 2. Prompt engineering
Topic 3: Data Preparation and Feature Engineering in Snowflake25%- Feature engineering techniques
  • 1. Scaling, encoding and normalization
  • 2. Feature creation and selection
  • 3. Using Snowflake functions for feature processing
- Data ingestion and integration
  • 1. Data cleaning and transformation
  • 2. Structured and semi-structured data handling
Topic 4: Model Deployment, Monitoring and Governance15%- Governance and compliance
  • 1. Lineage and audit
  • 2. Security and access control
- Monitoring and maintenance
  • 1. Data drift and model drift detection
  • 2. Performance tracking
- Deployment strategies
  • 1. Batch and real-time inference
  • 2. Model serving in Snowflake
Topic 5: Data Science Concepts and Methodologies20%- Data science lifecycle
  • 1. Data collection and acquisition
  • 2. Exploratory data analysis
  • 3. Problem framing and requirements
- Statistical and mathematical foundations
  • 1. Probability and statistics
  • 2. Evaluation metrics


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