SnowPro Advanced: Data Scientist (DSA-C03) Practice Test
Gana confianza para SnowPro Advanced: Data Scientist (DSA-C03). Practica los conceptos, comprende las respuestas y refuerza tus conocimientos pregunta a pregunta.
Probar una preguntaDescripción y detalles del examen
The SnowPro Advanced: Data Scientist (DSA-C03) certification is Snowflake's advanced-level credential designed for data science professionals who build, deploy, and operationalize machine learning and generative AI solutions natively within the Snowflake Data Cloud. This 71-question exam validates a candidate's ability to apply data science concepts end-to-end: from framing business problems and preparing data, through feature engineering and model training, to deploying models and leveraging Snowflake's GenAI and LLM capabilities such as Snowflake Cortex, Snowpark ML, and Model Registry. Unlike vendor-neutral data science certifications, this exam is tightly scoped to Snowflake's ecosystem, testing practical fluency with Snowpark Python, SQL-based transformations, Snowflake Notebooks, Feature Store, and governed AI workflows. Earning this certification signals that a practitioner can move beyond experimentation into production-grade, governed AI delivery on a modern cloud data platform. It is particularly valuable for data scientists, ML engineers, and analytics professionals who want to demonstrate platform-specific expertise that directly maps to enterprise AI initiatives. The credential complements foundational SnowPro certifications and positions holders for roles where Snowflake is the system of record for both data and AI workloads.
Temario 1.0
Preguntas de Muestra
Elige una respuesta y consulta la explicación para ver cómo funciona la práctica.
60 de 71 respuestas incluyen una referencia verificable.
A data scientist is using Snowpark to transform a DataFrame and needs to specify which columns to include in the result. What must they provide when calling the transformation method?
A data scientist needs to join a Snowpark DataFrame with itself on different columns, such as matching an id column with a parent_id column. Using a single DataFrame for the self-join fails. What should the data scientist do to perform the self-join successfully?
A data scientist is writing a multi-threaded Snowpark Python application where several threads share a single session object. Each thread needs to use a different warehouse and database. What should the developer do to avoid unexpected behavior?
A data scientist is developing a Snowpark stored procedure that submits multiple independent queries from separate Python threads. What is the effect of Snowpark's management of the Global Interpreter Lock (GIL) in this scenario?
A data scientist is reviewing Snowflake documentation to find a guide that demonstrates integrating Feature Store with pipelines in a more advanced way than the basic API overview. Which resource should they select?
Qué temas cubre este examen
01GenAI and LLM capabilities
Temas
- Use GenAI and LLM capabilities in Snowflake
Objetivos de aprendizaje
- Use GenAI and LLM capabilities in Snowflake
02Outline data science concepts
Temas
- Outline data science concepts
Objetivos de aprendizaje
- Outline data science concepts
03Prepare data and feature engineering
Temas
- Prepare data and use feature engineering in Snowflake
Objetivos de aprendizaje
- Prepare data and use feature engineering in Snowflake
04Snowflake data science best practices
Temas
- Implement Snowflake data science best practices
Objetivos de aprendizaje
- Implement Snowflake data science best practices
05Train and use machine learning models
Temas
- Train and use machine learning models
Objetivos de aprendizaje
- Train and use machine learning models