SnowPro® Advanced Data Scientist

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    Training Mode: Online

    Description

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

    SNOWPRO® ADVANCED: DATA SCIENTIST OVERVIEW

    The SnowPro® Advanced: Data Scientist Certification 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

    SNOWPRO ADVANCED: DATA SCIENTIST CANDIDATE

    2+ years of practical data science experience with Snowflake, in an enterprise environment. In addition, successful candidates may have:

    • A statistical, mathematical, or science education (or equivalent work experience)
    • Background working with one or more of the following programming languages (e.g., Python, R, SQL, PySpark, etc.)
    • Experience modeling and using machine learning platforms (e.g., SageMaker, Azure Machine Learning, GCP AI platform, AutoML tools, etc.)
    • An understanding of various open source and commercial frameworks and libraries (e.g., scikit-learn, TensorFlow, etc. )
    • Experience preparing, cleaning, and transforming data sets from multiple sources
    • Experience creating features for machine learning training
    • Experience validating and interpreting models
    • Experience putting a model into production and monitoring the model in production
    • Experience presenting data using visualization tools

    Target Audience:

    • Data Scientists
    • AI/ML Engineers

    EXAM FORMAT

    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
    Unscored Content: Exams may include unscored items to gather statistical information for future use. These items are not identified on the form and do not impact your score, and additional time is factored into account for this content.
    Prerequisites: SnowPro Core Certified
    Delivery Options:

    1. Online Proctoring
    2. Onsite Testing Centers

    EXAM DOMAIN BREAKDOWN

    This exam guide includes test domains, weightings, and objectives. It is not a comprehensive listing of all the content that will be presented on this examination. The table below lists the main content domains and their weightings.

    DomainDomain Weightings on Exams
    1.0 Data Science Concepts15-20%
    2.0 Data Pipelining15-20%
    3.0 Data Preparation and Feature Engineering30-35%
    4.0 Model Development15-20%
    5.0 Model Deployment15-20%

    EXAM TOPICS

    Outlined below are the Domains & Objectives measured on the exam. To view subtopics, download the exam study guide.

    Domain 1.0: Data Science Concepts

    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.

    Domain 2.0: Data Pipelining

    2.1 Enrich data by consuming data sharing sources.
    2.2 Build a data science pipeline.

    Domain 3.0: Data Preparation and Feature Engineering

    3.1 Prepare and clean data in Snowflake.
    3.2 Perform exploratory data analysis in Snowflake.
    3.3 Perform feature engineering on Snowflake data.
    3.4 Visualize and interpret the data to present a business case.

    Domain 4.0: Model Development

    4.1 Connect data science tools directly to data in Snowflake.
    4.2 Train a data science model.
    4.3 Validate a data science model.
    4.4 Interpret a model.

    Domain 5.0: Model Deployment

    5.1 Move a data science model into production.
    5.2 Determine the effectiveness of a model and retrain if necessary.
    5.3 Outline model lifecycle and validation tools

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