SnowPro® Advanced Data Scientist Recertification

Duration: Hours



    Training Mode: Online


    The SnowPro Advanced: Data Scientist Recertification exam is available for candidates with an expiring SnowPro Advanced: Data Scientist Certification.


    The SnowPro® Advanced: Data Scientist Recertification exam is available for candidates with an expiring SnowPro Advanced: Data Scientist Certification.

    By passing the SnowPro Advanced: Data Scientist Recertification, a candidate’s SnowPro Core Certification + SnowPro Advanced: Data Scientist status will extend an additional 2 years from the date of passing the recertification exam.

    Candidates must hold a valid SnowPro Advanced: Data Scientist Certification at the time they take the recertification exam to be eligible.


    Exam Version: DSA-R02
    Total Number of Questions: 40
    Question Types: Multiple Select, Multiple Choice
    Time Limit: 85 minutes
    Language: English
    Registration fee: $188 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 & SnowPro Advanced: Data Scientist Certified
    Delivery Options:

    1. Online Proctoring
    2. Onsite Testing Centers


    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%


    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

    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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