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More about DP-900 Exam
In brief, DP-900 test focuses on exam objectives like interacting with relational data on Azure, handling analytics workload on Azure, dealing with non-relational data on Azure, and knowing the central data concepts needed for Azure. The Microsoft DP-900 test, in particular, is designed for those who just started to work with cloud-related data. Those planning to take DP-900 should already possess basic knowledge of the main data-related concepts and understand how those concepts can be deployed with the help of Microsoft Azure data services. Also, candidates for DP-900 should be no strangers to analytical data workloads, transactional data workloads, non-relational data, and relational data. When it comes to the exam details, there are no mandatory prerequisites needed for DP-900. The signup cost of this fundamental-level exam is USD 99, plus it is administered around the globe in countless tongues like Spanish, French, German, Korean, Chinese, Japanese, and English. To know more, DP-900 is conducted at nearby exam centers or on the web in a supervised setting. As it was claimed above, passing this single exam means candidates will be awarded the Microsoft Certified: Azure Data Fundamentals validation, which is renewable. After attaining this certificate, learners can chase higher-level Microsoft certifications in the future like the Microsoft Certified: Azure Data Engineer Associate or the Microsoft Certified: Azure Database Administrator Associate.
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 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.
- 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.
Objective of Microsoft DP-900
The Microsoft Data Protection and Management Fundamentals exam is just one of the many exams dedicated to data management. Test your skills and knowledge in the area of data protection and management, as well as improve your skills. Pay attention to the latest changes in this field of technology. Skilling up to your skills, you must be aware of the latest changes in the technology field to become successful. Collection, integration, analysis and distribution of data is a great way to gain insight into the latest challenges in the field of data protection and management. Microsoft DP-900 exam dumps the most qualified candidates will be able to pass this exam easily. Accurate data collection, this data will be stored in a database. Integration of different systems, this data will be integrated with another database to analyze the data. Outline and analysis of data, also described in an outline. Distribution and distribution of data, this data will be sent to the correct locations and users for their use. Feedback and changes, the data will be analyzed for changes and feedback. Command and control, a command will be given to a list of specific users and systems to manipulate the data. Subscribe and receive data, the data will be sent to the appropriate users. Dashboards and reports and alerts, the data collected can be converted into a report or alert. Loading and storage and user access and BPA and cloud and networks and servers and operating systems, the candidates will increase their skills in data management.
Knowing how to process and use data is a very valuable skill in the modern IT world. Blob storage and data management, a quick and easy way to store files. Workflow and logic and data integration and export and SAP and SQL and SharePoint and webforms, the candidates will increase their skill in Microsoft cloud technologies. Solve problems and monitoring and forensics and management, the candidates will increase their skills in data protection. Outdated backups and DR solutions and paper backup and Oracle and Unix and security and virtualization, the candidates will increase their skills in data protection. Roles and responsibilities and training and policies and procedures, the candidates will increase their skills in data protection. Paid and volunteer and onsite and offsite and mobile and onsite and removable and disaster recovery and disaster recovery solutions, the candidates will increase their skills in data protection. Digital and paper and data and sensitive and compliance and eDiscovery, the candidates will increase their skills in data protection. Cognitive and user and cloud and non-volatile and network and mobile and software as a service and storage as a service, the candidates will increase their skills in data protection.
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/dp-900
Microsoft DP-900 Exam Syllabus Topics:
| Topic | Details |
|---|---|
Describe core data concepts (15-20%) | |
| Describe types of core data workloads | - describe batch data - describe streaming data - describe the difference between batch and streaming data - describe the characteristics of relational data |
| Describe data analytics core concepts | - describe data visualization (e.g., visualization, reporting, business intelligence (BI)) - describe basic chart types such as bar charts and pie charts - describe analytics techniques (e.g., descriptive, diagnostic, predictive, prescriptive, cognitive) - describe ELT and ETL processing - describe the concepts of data processing |
Describe how to work with relational data on Azure (25-30%) | |
| Describe relational data workloads | - identify the right data offering for a relational workload - describe relational data structures (e.g., tables, index, views) |
| Describe relational Azure data services | - describe and compare PaaS, IaaS, and SaaS solutions - describe Azure SQL family of products including Azure SQL Database, Azure SQL Managed Instance, and SQL Server on Azure Virtual Machines - describe Azure Synapse Analytics - describe Azure Database for PostgreSQL, Azure Database for MariaDB, and Azure Database for MySQL |
| Identify basic management tasks for relational data | - describe provisioning and deployment of relational data services - describe method for deployment including the Azure portal, Azure Resource Manager templates, Azure PowerShell, and the Azure command-line interface (CLI) - identify data security components (e.g., firewall, authentication) - identify basic connectivity issues (e.g., accessing from on-premises, access with Azure VNets, access from Internet, authentication, firewalls) - identify query tools (e.g., Azure Data Studio, SQL Server Management Studio, sqlcmd utility, etc.) |
| Describe query techniques for data using SQL language | - compare Data Definition Language (DDL) versus Data Manipulation Language (DML) - query relational data in Azure SQL Database, Azure Database for PostgreSQL, and Azure Database for MySQL |
Describe how to work with non-relational data on Azure (25-30%) | |
| Describe non-relational data workloads | - describe the characteristics of non-relational data - describe the types of non-relational and NoSQL data - recommend the correct data store - determine when to use non-relational data |
| Describe non-relational data offerings on Azure | - identify Azure data services for non-relational workloads - describe Azure Cosmos DB APIs - describe Azure Table storage - describe Azure Blob storage - describe Azure File storage |
| Identify basic management tasks for non-relational data | - describe provisioning and deployment of non-relational data services - describe method for deployment including the Azure portal, Azure Resource Manager templates, Azure PowerShell, and the Azure command-line interface (CLI) - identify data security components (e.g., firewall, authentication, encryption) - identify basic connectivity issues (e.g., accessing from on-premises, access with Azure VNets, access from Internet, authentication, firewalls) - identify management tools for non-relational data |
Describe an analytics workload on Azure (25-30%) | |
| Describe analytics workloads | - describe transactional workloads - describe the difference between a transactional and an analytics workload - describe the difference between batch and real time - describe data warehousing workloads - determine when a data warehouse solution is needed |
| Describe the components of a modern data warehouse | - describe Azure data services for modern data warehousing such as Azure Data Lake Storage Gen2, Azure Synapse Analytics, Azure Databricks, and Azure HDInsight - describe modern data warehousing architecture and workload |
| Describe data ingestion and processing on Azure | - describe common practices for data loading - describe the components of Azure Data Factory (e.g., pipeline, activities, etc.) - describe data processing options (e.g., Azure HDInsight, Azure Databricks, Azure Synapse Analytics, Azure Data Factory) |
| Describe data visualization in Microsoft Power BI | - describe the role of paginated reporting - describe the role of interactive reports - describe the role of dashboards - describe the workflow in Power BI |
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Microsoft DP-900日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Relational Data in Azure | 20-25% | - Relational data concepts - Azure relational data services
|
| Core Data Concepts | 25-30% | - Data analytics workloads overview - Roles and responsibilities in data work - Types of core data workloads - Relational vs non-relational data |
| Non-Relational Data in Azure | 15-20% | - Data processing options for non-relational data - Azure storage services
|
| Analytics Workloads in Azure | 25-30% | - Data visualization and reporting - Data analytics concepts - Azure analytics services
|
