Snowflake · SF-DS · Advanced
SnowPro Advanced: Data Scientist (DSA-C03) — Practice Questions and Mock Exam
Practice real SF-DS questions, never dumps. Alex explains every answer, and your readiness score tells you when you're ready to pass.
Checked against Snowflake · April 2026Current exam version
Overview
About the exam
The SnowPro Advanced: Data Scientist Certification (DSA-C03) validates the ability to apply data science principles within Snowflake, including defining data science concepts, performing data and feature engineering, building and deploying machine learning models, leveraging Snowpark for ML workflows, and presenting results through data visualization. It covers the complete model lifecycle from data preparation to production deployment.
This certification is ideal for data scientists, ML engineers, and quantitative researchers with two or more years of experience using Snowflake for data science workloads. It demonstrates proficiency in building end-to-end ML solutions natively on Snowflake and validates skills increasingly in demand as organizations operationalize AI.
Exam Domains
What's on the exam
The exam consists of 65 questions — multiple-choice, multiple-select, and true/false — to be completed in 115 minutes. Questions cover five domains: Data & Feature Engineering (30%), Model Development (20%), Data Pipelining (19%), Model Deployment (16%), and Data Science Concepts (15%). A passing score is 750 out of 1000. Feature engineering questions are the heaviest section — allocate your study time accordingly.
Apply data science methodologies, statistics, and ML fundamentals within the Snowflake ecosystem.
Prepare data, engineer features, and handle data quality using Snowflake SQL, Snowpark, and ML functions.
Build, train, and evaluate ML models using Snowpark ML, Snowflake ML functions, and integrated frameworks.
Deploy, monitor, and manage ML models in production using Snowflake model registry and serving infrastructure.
SourceSnowflake exam page
Format
What to expect
Watch out
Where candidates struggle
Data scientists who use Snowflake only for SQL queries and haven't practiced Snowpark ML, stored procedures for model training, or Snowflake's model registry often struggle with deployment and lifecycle questions.
- 01Snowpark ML vs External — Not knowing when to use Snowpark ML (native, runs in Snowflake) versus external ML frameworks with Snowflake as a data source leads to wrong architecture answers.
- 02Feature Store — Confusing Snowflake feature store capabilities with external feature stores and not understanding how to manage feature pipelines natively leads to engineering question errors.
- 03Model Registry — Not understanding how Snowflake Model Registry works for versioning, deploying, and managing ML models leads to incorrect lifecycle management answers.
- 04UDFs for Inference — Misunderstanding how to use Python UDFs and vectorized UDFs for model inference at scale within Snowflake causes deployment pattern mistakes.
- 05Data Leakage — Not recognizing common data leakage patterns in feature engineering — such as using future data or target encoding without proper splits — leads to wrong methodology answers.
Details
Exam logistics
Delivered online via the Snowflake Certification Portal. Available in English and Japanese. The certification is valid for 2 years. Renewal requires recertification or continuing education credits. Exam fee is $375 USD. Prerequisite: active SnowPro Core certification.
SourceSnowflake exam page
Before you book the exam
Would you pass SF-DS today?
Take the free readiness check. Answer real SF-DS questions and get your readiness score across every domain.
Take the free readiness check20 questions · freeReach 80% readiness by exam day. Pass, or your money back.