Microsoft DP-900日本語 Exam Syllabus Topics:
| Topic | Details |
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| Topic 1 | - Describe an analytics workload: This topic introduces aspiring Microsoft data engineers to the components of large-scale analytics, such as data ingestion, transformation, and querying. It also discusses real-time data analytics considerations, including low-latency processing with Azure Stream Analytics. Finally, data visualization with Microsoft Power BI, focusing on interactive dashboards and reports, is covered.
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| Topic 2 | - Identify considerations for relational data on Azure: This topic provides aspiring Microsoft data engineers with a clear understanding of relational data concepts, including schemas, tables, keys, and normalization. It further explores relational Azure data services such as Azure SQL Database and Azure Database for MySQL, highlighting their use cases and capabilities.
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| Topic 3 | - Describe considerations for working with non-relational data on Azure: Aspiring Microsoft data engineers gain insight into Azure's non-relational data capabilities in this topic. It discusses Azure Storage options, including Blob, Table, and Queue Storage, suitable for diverse scenarios. Additionally, the features and strengths of Azure Cosmos DB, such as global distribution and multi-model support, are outlined.
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| Topic 4 | - Describe core data concepts: In this topic, aspiring Microsoft data engineers explore fundamental ways to represent data, such as structured, semi-structured, and unstructured formats. Additionally, the topic explains various data storage options, emphasizing file, database, and object storage. It also details common data workloads, including transactional, analytical, and streaming workloads. Lastly, the roles and responsibilities for managing data workloads, such as administrators, engineers, and analysts, are clarified.
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Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/dp-900
Microsoft DP-900日本語 Exam Overview:
| Certification Vendor: | Microsoft |
| Exam Name: | Microsoft Azure Data Fundamentals |
| Exam Number: | DP-900 |
| Exam Format: | Scenario-based, Multiple-choice |
| Certificate Validity Period: | N/A |
| Related Certifications: | Microsoft Certified: Azure Data Fundamentals |
| Passing Score: | 700 / 1000 |
| Exam Duration: | 65 minutes |
| Exam Price: | USD 99 |
| Available Languages: | Russian, Chinese (Simplified), Spanish, Portuguese (Brazil), English, Japanese, Arabic (Saudi Arabia), Korean, German, Italian, Indonesian (Indonesia), Chinese (Traditional), French |
| Real Exam Qty: | 40-60 |
| Sample Questions: | Microsoft DP-900日本語 Sample Questions |
| Exam Way: | Pearson VUE |
| Pre Condition: | There are no prerequisites for this exam. |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/resources/study-guides/dp-900 |
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.
- 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.
- 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.
The Microsoft DP-900 (Microsoft Azure Data Fundamentals) is the single exam students will have to overcome if they want to attain the Microsoft Certified: Azure Data Fundamentals certificate. Those who hold this certification can become a developer, data engineer, data administrator, or data analyst.
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