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Snowflake · SF-DS · Advanced

SnowPro Advanced: Data Scientist (DSA-C03)

Validates advanced knowledge for applying data science and machine learning principles using Snowflake including Snowpark ML, model development, and deployment. 65+ AI-generated practice questions with explanations. Free trial, pass guarantee.

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65 Questions
115min Time Limit
750/ 1000 Pass Score
$375 USD Exam Fee

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.

Data Science Concepts 17%

Apply data science methodologies, statistics, and ML fundamentals within the Snowflake ecosystem.

Data Preparation and Feature Engineering 27%

Prepare data, engineer features, and handle data quality using Snowflake SQL, Snowpark, and ML functions.

Model Development 31%

Build, train, and evaluate ML models using Snowpark ML, Snowflake ML functions, and integrated frameworks.

Model Deployment 25%

Deploy, monitor, and manage ML models in production using Snowflake model registry and serving infrastructure.

Format

What to expect

multiple choice
70%
multiple response
30%

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.

  1. 01
    Snowpark 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.
  2. 02
    Feature Store — Confusing Snowflake feature store capabilities with external feature stores and not understanding how to manage feature pipelines natively leads to engineering question errors.
  3. 03
    Model Registry — Not understanding how Snowflake Model Registry works for versioning, deploying, and managing ML models leads to incorrect lifecycle management answers.
  4. 04
    UDFs for Inference — Misunderstanding how to use Python UDFs and vectorized UDFs for model inference at scale within Snowflake causes deployment pattern mistakes.
  5. 05
    Data 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.

Delivery Online proctored or onsite testing centers.
Retake policy No waiting period between attempts. Full registration fee required for each attempt.
Validity 2 years
Career outcomes Senior Data Scientist, ML Engineer, AI Architect, Snowflake ML Specialist, Data Science Lead.
Renewal Pass the current version of the SnowPro Advanced: Data Scientist exam to recertify every 2 years.
Study time ~80 hours
Official guide View on vendor site

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