SnowPro® Advanced Data Scientist Recertification Exam Certification Training

Duration: Hours



    Training Mode: Online



    The SNOWPRO® Advanced: Data Scientist Recertification Exam Certification Training offers a comprehensive overview for professionals seeking to renew their certification in data science expertise within the Snowflake ecosystem. This training program is meticulously designed to ensure that individuals stay updated with the latest advancements and best practices in data science.

    Training Content:

    Participants in this program review essential concepts, methodologies, and tools relevant to data science within the Snowflake platform. The curriculum covers a wide range of topics, including data manipulation, machine learning, data visualization, and advanced analytics techniques.

    Professional Enhancement:

    By completing the SNOWPRO® Advanced: Data Scientist Recertification Exam Certification Training, professionals reaffirm their proficiency in leveraging Snowflake for data science tasks, enhancing their credibility and value in the data science field. This training program serves as a testament to their commitment to maintaining expertise and staying competitive in the rapidly evolving landscape of data science.

    Exam: Extension and Eligibility

    1. The SnowPro® Advanced: Data Scientist Recertification exam is available for candidates with an expiring SnowPro Advanced: Data Scientist Certification.
    2. 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.
    3. Candidates must hold a valid SnowPro® Advanced: Data Scientist Certification at the time they take the recertification exam to be eligible.

    Exam Format:

    1. Exam Version: DSA-R02
    2. Total Number of Questions: 40
    3. Question Types: Multiple Select, Multiple Choice
    4. Time Limit: 85 minutes
    5. Language: English
    6. Registration fee: USD 188
    7. 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.


    SnowPro® Core Certified & SnowPro® Advanced: Data Scientist Certified
    Delivery Options:

    1. Online Proctoring
    2. Onsite Testing Centers

    Advanced 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 in 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.

    SnowPro® Domain:

    Data Science Concepts

    1. Define machine learning concepts for data science workloads.
    2. Outline machine learning problem types.
    3. Summarize the machine learning lifecycle.
    4. Define statistical concepts for data science.

    Data Pipelining

    1. Enrich data by consuming data-sharing sources.
    2. Build a data science pipeline.

    Data Preparation and Feature Engineering

    1. Prepare and clean data in Snowflake.
    2. Perform exploratory data analysis in Snowflake.
    3. Perform feature engineering on Snowflake data.
    4. Visualize and interpret the data to present a business case.

    Model Development

    1. Connect data science tools directly to data in Snowflake.
    2. Train a data science model.
    3. Validate a data science model.
    4. Interpret a model.

    Model Deployment

    1. Move a data science model into production.
    2. Determine the effectiveness of a model and retrain if necessary.
    3. Outline model lifecycle and validation tools.


    1. Data science concepts
    2. Snowflake data science best practices
    3. Preparing data and feature engineering in Snowflake
    4. Training and using machine learning models
    5. Using data visualization to present a business case
    6. Implementing model lifecycle management
    7. Connecting data science tools directly to Snowflake
    8. Optimizing models
    9. Determining the effectiveness of models in production
    10. Validating models accurately

    For more information on SnowPro® Advanced Data Scientist Recertification Exam Certification Training; please visit here.

    Contact Locus IT support team for further training details.




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