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Exam DP-900: Microsoft Azure Data Fundamentals

Candidates for this exam should have foundational knowledge of core data concepts and how they are implemented using Microsoft Azure data services.

This exam is intended for candidates beginning to work with data in the cloud.

Candidates should be familiar with the concepts of relational and non-relational data, and different types of data workloads such as transactional or analytical.

Azure Data Fundamentals can be used to prepare for other Azure role-based certifications like Azure Database Administrator Associate or Azure Data Engineer Associate, but it’s not a prerequisite for any of them.

Part of the requirements for: Microsoft Certified: Azure Data Fundamentals

Download exam skills outline

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Workload analytics on Azure

  • Analytics workloads – under this topic, candidates should learn more about transactional workloads as well as data warehousing workloads. Also, they have to get skilled in differentiating between an analytics and a transactional workload, know the difference between real and batch time, and be able to determining when a data warehouse solution is required.
  • Date processing and ingestion on Azure – main topics within this subdomain include data loading common practices, Azure Data Factory components (activities, pipeline, etc.), and data processing.
  • Elements of a modern data warehouse – this sub-section is focused on Azure data services related to modern data warehousing. In addition, applicants should be familiar with modern data warehousing workload and architecture.
  • Data visualization in Microsoft Power BI – this topic requires candidates to learn more about paginated reporting, dashboards, interactive reports, as well as workflow in Power BI.

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The Microsoft DP-900 test evaluates the ability of the candidates to execute specific technical tasks. The examinees must understand these areas before they take this test. The topics that this certification exam covers are as follows:

Explain the Concepts of Core Data: 15-20%

  • Explain the Core Concepts of Data Analytics: The candidates need to be able to explain data visualization, analytics techniques, data processing concepts, ETL and ELT processing, as well as the basic types of charts, including pie charts and bar charts.
  • Explain Core Data Workloads Types: The test takers should be able to explain batch data, attributes of relational data, streaming data, and the differences between streaming and batch data.

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Microsoft Azure Data Fundamentals Exam Certification Details:

Schedule ExamPearson VUE
Sample QuestionsMicrosoft Azure Data Fundamentals Sample Questions
Passing Score700 / 1000
Books / TrainingDP-900T00-A: Microsoft Azure Data Fundamentals
Exam NameMicrosoft Certified - Azure Data Fundamentals
Exam Price$99 (USD)
Number of Questions40-60
Duration60 mins
Exam CodeDP-900

Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/dp-900

Microsoft DP-900 Deutsch Exam Syllabus Topics:

SectionWeightObjectives
Describe considerations for working with non-relational data on Azure15–20%- Describe Azure Cosmos DB
  • 1. Consistency levels and partitioning
    • 2. APIs and data models
      • 3. Capabilities and features
        - Describe Azure storage services
        • 1. File Storage
          • 2. Queue Storage
            • 3. Blob Storage
              • 4. Table Storage
                - Describe non-relational data concepts
                • 1. Characteristics of NoSQL data
                  • 2. Use cases for non-relational data
                    Describe an analytics workload on Azure25–30%- Describe real-time analytics
                    • 1. Stream processing concepts
                      • 2. Azure Stream Analytics
                        - Describe big data and analytics concepts
                        • 1. Data lakes and data warehouses
                          • 2. Modern data warehouse architecture
                            • 3. Data ingestion and processing
                              - Describe Azure analytics services
                              • 1. Azure Databricks
                                • 2. Azure HDInsight
                                  • 3. Azure Data Lake Storage Gen2
                                    • 4. Microsoft Fabric
                                      • 5. Azure Synapse Analytics
                                        - Describe data visualization
                                        • 1. Data models and reports
                                          • 2. Power BI capabilities
                                            Identify considerations for relational data on Azure20–25%- Describe relational data concepts
                                            • 1. Normalization
                                              • 2. Query data with SQL
                                                • 3. Relationships, keys, constraints
                                                  - Describe Azure relational data services
                                                  • 1. SQL Server on Azure Virtual Machines
                                                    • 2. Open-source databases: PostgreSQL, MySQL, MariaDB
                                                      • 3. Azure SQL Database
                                                        • 4. Azure SQL Managed Instance
                                                          Describe core data concepts25–30%- Describe data workloads
                                                          • 1. Transactional workloads
                                                            • 2. Analytical workloads
                                                              • 3. Batch and streaming data
                                                                - Describe roles and responsibilities
                                                                • 1. Data engineer, data analyst, database administrator
                                                                  - Describe types of data
                                                                  • 1. Structured, semi-structured, and unstructured data
                                                                    • 2. File formats

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