The DP-900 certification exam covers a range of topics, including core data concepts, relational data services, non-relational data services, analytics services, and Azure data services. By passing the DP-900 exam, candidates demonstrate that they have a foundational understanding of data services and can use this knowledge to design and implement solutions that leverage the capabilities of Microsoft Azure. Microsoft Azure Data Fundamentals certification is a great starting point for individuals who are looking to begin a career in the cloud data services domain, as it provides a strong foundation for more advanced certifications in the field.
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/dp-900
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Retirement date: none
Prove that you can describe the following: core data concepts; how to work with relational data on Azure; how to work with non-relational data on Azure; and an analytics workload on Azure.
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The Microsoft DP-900 exam covers a range of topics including data storage, data processing, data analysis, and data security in the Azure environment. Candidates will be tested on their ability to plan, implement and monitor data solutions using various Azure services such as Azure Cosmos DB, Azure Data Factory, Azure SQL Database, and Azure Stream Analytics.
Microsoft DP-900 exam is a 60-minute exam that consists of 40-60 multiple-choice questions. DP-900 exam can be taken online or in-person at a testing center. The passing score for the exam is 700 out of 1000. DP-900 exam fee is $99, and it can be taken in multiple languages, including English, Chinese, German, Japanese, and Spanish.
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Microsoft DP-900 Exam Syllabus Topics:
| Section | Weight | Objectives |
| Describe core data concepts | 25-30% | - Identify options for data storage
- 1. Describe types of databases
- 2. Describe common formats for data files
- Describe ways to represent data
- 1. Describe features of unstructured data
- 2. Describe features of structured data
- 3. Describe features of semi-structured data
|
| Identify considerations for relational data on Azure | 20-25% | - Describe relational concepts
- 1. Identify features of relational data
- 2. Describe normalization
- Describe relational Azure data services
- 1. Describe Azure SQL Database
- 2. Describe Azure Database for MySQL
- 3. Describe SQL Server on Azure Virtual Machines
- 4. Describe Azure Database for PostgreSQL
- 5. Describe Azure Database for MariaDB
- 6. Describe Azure SQL family of products
- 7. Describe Azure SQL Managed Instance
|
| Describe considerations for working with non-relational data on Azure | 15-20% | - Describe non-relational Azure data services
- 1. Describe Azure Blob storage
- 2. Describe Azure File storage
- 3. Describe Azure Cosmos DB
- 4. Describe Azure Table storage
- Describe non-relational concepts
- 1. Identify non-relational data
- 2. Describe non-relational data offerings
|
| Describe an analytics workload on Azure | 25-30% | - Describe analytics concepts
- 1. Describe ETL and ELT processes
- 2. Describe data warehousing
- 3. Describe transactional workloads
- 4. Describe analytical workloads
- Describe data visualization in Microsoft Power BI
- 1. Describe Power BI reports and dashboards
- 2. Describe Power BI
- 3. Describe Power BI tools and workflow
- Describe Azure data analytics services
- 1. Describe Microsoft Fabric
- 2. Describe Azure Synapse Analytics
- 3. Describe Azure Stream Analytics
- 4. Describe Azure HDInsight
- 5. Describe Azure Data Factory
- 6. Describe Azure Databricks
|