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      Adobe

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      • Adobe Experience Platform – ADOBE QUALIFIED

      Adobe Experience Platform – ADOBE QUALIFIED

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      VLT
      Adobe

      On Demand $1,495.00

      Virtual Live







      adobe exp platform
      • Overview
      • Curriculum
      • Instructor

      Course Description

      The Adobe Qualified – Adobe Experience Platform exam guide provides potential candidates with the information they need to prepare for the exam.

      Individuals who take this exam have industry experience and then learn Adobe Experience Platform. Typical job titles for individuals preparing for this exam include: Data Architect/Modeler, Data Analyst, Data Engineer, Data Science Engineer, Data Scientist

      Prerequisite

      At a minimum, candidates should have knowledge of the following:

      • Browse through the Experience Platform UI
      • High-level understanding of each UI component
      • Identify schemas, classes, and mixins
      • Create datasets
      • Use the mapper workflow
      • Ingest data
      • Create basic segments
      • Have an understanding of the Adobe Experience Platform application services
      • Understand the feature/functionality related to retrieving profiles via an API
      • Execute a query
      • You should already have industry knowledge in Database tools/BI tools, Data Science modeling tools
      •  You should be familiar with how data relationships work, and also with ETL (Extract Transform and Load)

      Familiarity with the following:

      • Microsoft Excel
      • Postman
      • Power BI

      Course Features

      • Lectures 9
      • Quizzes 0
      • Duration 40 hours
      • Skill level All levels
      • Language English
      • Students 426
      • Certificate No
      • Assessments Yes
      CoursesAdobeAdobe Experience Platform – ADOBE QUALIFIED
      • Section 1: Data Modeling
        2
        • Lecture1.1
          You should be able to describe the types of classes used when modeling data. You should also be able to describe the components that make up a schema.
        • Lecture1.2
          You should be able to explain when to use out-of-the-box components and when to use custom components. You should be able to explain how a schema is used to build a union schema and a unified profile. You should also be able to explain how identities are used in an ID Graph
      • Section 2: Data Loading
        2
        • Lecture2.1
          You should be able to list and describe ingestion types, and also describe the basic functionality associated with connectors
        • Lecture2.2
          You should be able to identify errors associated with data loading (monitoring), explain how to validate data that has been loaded, and explain dataset configuration
      • Section 3: Data Analysis
        3
        • Lecture3.1
          You should be able to demonstrate the ability to write and execute queries
        • Lecture3.2
          You should be able to describe the Customer Journey Analytics application service
        • Lecture3.3
          You should be able to explain how Data Science Workspace (DSW) uses and writes to datasets and a unified profile
      • Section 4: Data Activation
        2
        • Lecture4.1
          You should be able to explain how to create and evaluate segments
        • Lecture4.2
          You should be able to describe Real Time CDP
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