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 Price: | $375 USD |
| Exam Format: | Multiple Choice, Multiple Select, Scenario-based Questions |
| Exam Duration: | 115 minutes |
| Real Exam Qty: | 65 |
| Related Certifications: | SnowPro Core Certification
SnowPro Advanced: Data Scientist |
| Available Languages: | English |
| Passing Score: | 750/1000 |
| 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:
| Topic | Details |
|---|
| Topic 1 | - Data Science Concepts: This portion of the test includes basic machine learning principles, problem types, the machine learning lifecycle, and statistical ideas that are crucial for data science workloads for analysts and data scientists. It guarantees that applicants comprehend data science theory inside the framework of Snowflake's platform.
|
| Topic 2 | - Model Deployment: For MLOps engineers and data scientists, this domain covers the process of moving models into production, assessing model effectiveness, retraining models, and understanding model lifecycle management tools. It ensures candidates can operationalize machine learning models in a Snowflake-based production environment.
|
| Topic 3 | - Data Preparation and Feature Engineering: This section of the test includes data cleansing, exploratory data analysis, feature engineering, and data visualization using Snowflake for data analysts and machine learning developers. It evaluates proficiency in data preparation for model building and stakeholder presentation.
|
| Topic 4 | - Data Pipelining: This domain focuses on creating efficient data science pipelines and enhancing data through data-sharing sources for data engineers and ETL specialists. It evaluates the capacity to establish reliable data flows throughout the ecosystem of Snowflake.
|
Reference: https://learn.snowflake.com/en/certifications/snowpro-advanced-datascientist/
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