Free Snowflake DSA-C03 Exam Questions
SnowPro® Advanced: Data Scientist (DSA-C03)
Practice with our comprehensive collection of free SnowPro® Advanced: Data Scientist (DSA-C03) exam questions. All questions are aligned with the latest exam guide and include detailed explanations to help you master the material.
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Exam Information
Exam Details
Complete information about the SnowPro Advanced: Data Scientist (DSA-C03) certification exam
Scenario-based questions
120 minutes (2 hours)
2 years
Online or test center
Prerequisites: Eligible individuals must hold an active SnowPro Core Certified credential.
Exam Topics & Skills Assessed
Key domains and advanced skills covered in the SnowPro Advanced: Data Scientist exam
Core Snowflake & AI/ML Capabilities:
- Snowpark for Python and SQL, Snowpark ML, Python connector with pandas support, Spark connector
- Snowflake Notebooks and Snowsight for EDA, visualization, and ad-hoc analysis
- Statistical and analytic functions (window functions, variance, stddev, TOPN, approximation functions)
- Feature engineering: scaling, encoding, normalization, binning, one-hot encoding, Feature Store
- ML lifecycle: data collection, visualization/exploration, feature engineering, training, deployment, monitoring
- GenAI and LLM with Snowflake Cortex: vector embeddings, prompt engineering, fine-tuning, task models
- Model deployment: Python UDFs/UDTFs, external functions, Model Registry, Snowpark Container Services
- Model monitoring and governance: metrics (AUC, ROC, RMSE), drift/decay, tagging, versioning, automation
Exam Sections (4 Main Domains with Weightings):
- Data Science Concepts (17%) — ML concepts (supervised/unsupervised), problem types (regression, classification, clustering, association), ML lifecycle, statistical concepts (distributions, CLT, Z/T tests, confidence intervals)
- Data Preparation and Feature Engineering (27%) — Prepare/clean data with Snowpark & SQL (joins, deduplication, casting, handling missing values, sampling), EDA and descriptive statistics, preprocessing (scaling, encoding, normalization, binning/one-hot), Feature Store, visualization and interpretation with Snowsight and notebooks
- Model Development (31%) — Connect tools to Snowflake (Snowpark, Python connector, IDEs), leverage GenAI/LLM (Cortex embeddings, prompting, fine-tuning, task models), build pipelines (dynamic tables, Python UDF/UDTF, stored procedures), hyperparameter tuning, metrics selection (log loss, AUC, RMSE), cross validation/hold-out, model validation (ROC/confusion matrix, residuals), model interpretation (feature impact, PDP, confidence intervals)
- Model Deployment (25%) — External hosted models via external functions, deploy models in Snowflake (Python UDFs, pre-built models, predictions storage, stage commands), Model Registry (logging/retrieval), container services, effectiveness and retraining (drift/decay, metrics), lifecycle tools (metadata tagging, versioning, automation of retraining)
About the SnowPro Advanced: Data Scientist Certification
The SnowPro Advanced: Data Scientist (DSA-C03) certification validates advanced knowledge and skills used to apply comprehensive data science principles, tools, and methodologies using Snowflake. The exam assesses the ability to outline core ML concepts and problem types, implement Snowflake best practices for data science, prepare and engineer features, train and validate models, and use GenAI/LLM capabilities with Snowflake Cortex.
Target candidates typically have 2+ years of practical data science experience with Snowflake in enterprise environments, backgrounds in statistics/mathematics/data science, and experience with languages such as Python and SQL. Beneficial experience includes ML platforms (e.g., SageMaker, Azure ML, GCP AI), frameworks such as scikit-learn and TensorFlow, preparing/cleaning/transforming datasets, creating features, validating and interpreting models, deploying to production, monitoring performance, and presenting insights with visualization tools.
An active SnowPro Core certification is required. The test uses scenario-based questions and real-world examples to evaluate applied proficiency across EDA, feature engineering, ML/GenAI development, deployment, monitoring, and governance on the Snowflake AI Data Cloud.