In order to obtain your SnowPro Advanced: Data Scientist Certification, it is essential to showcase your comprehensive knowledge and proficiency in utilizing fundamental concepts, tools, and methodologies of data science in conjunction with Snowflake. We are pleased to inform you that Passcert has recently released a set of newly cracked SnowPro Advanced: Data Scientist DSA-C02 Exam Dumps which are designed to assist you in thoroughly preparing for your upcoming test and ensuring a successful pass in your DSA-C02 exam. These SnowPro Advanced: Data Scientist DSA-C02 Exam Dumps have been meticulously crafted to cover all the necessary topics and provide you with the necessary resources to enhance your understanding and skills. With these exam dumps at your disposal, you can confidently approach your certification journey and achieve your desired outcome.

The SnowPro Advanced: Data Scientist Certification exam will test advanced knowledge and skills used to apply comprehensive data science principles, tools, and methodologies using Snowflake. This certification will test the ability to:

● Outline data science concepts

● Implement Snowflake data science best practices

● Prepare data and feature engineering in Snowflake

● Train and use machine learning models

● Use data visualization to present a business case (e.g., model explainability)

● Implement model lifecycle management

We recommend individuals have at least 2 + years of hands-on Snowflake Practitioner experience in a Data Scientist role prior to attempting this exam. The exam will assess skills through scenario-based questions and real-world examples.

Exam Version: DSA-C02

Total Number of Questions: 65

Question Types: Multiple Select, Multiple Choice

Time Limit: 115 minutes

Language: English

Registration fee: $375 USD

Passing Score: 750 + Scaled Scoring from 0 - 1000

Prerequisites: SnowPro Core Certified

Delivery Options: 1 Online Proctoring  2 Onsite Testing Centers

1.0 Data Science Concepts 15%

2.0 Data Pipelining 19%

3.0 Data Preparation and Feature Engineering 30%

4.0 Model Development 20%

5.0 Model Deployment 16%

1.1 Define machine learning concepts for data science workloads.

1.2 Outline machine learning problem types.

1.3 Summarize the machine learning lifecycle.

1.4 Define statistical concepts for data science.