PDF Version DemoHigh level topics covered by our practice test
This Web Simulator is for Microsoft data management professionals, data architects, data scientists, and data developers who design big data analytics solutions on Microsoft Azure. Candidates for this exam will have relevant work experience in big data analytics solutions.
Reference: https://www.microsoft.com/en-pk/learning/exam-70-475.aspx
Our 70-475 exam dumps will include those topics:
- Operationalize end-to-end cloud analytics solutions (30-35%)
- Design big data real-time processing solutions (30-35%)
- Design big data batch processing and interactive solutions (30-35%)
For more info visit: Microsoft Official 70-475 Exam Reference
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70-475 - Designing and Implementing Big Data Analytics Solutions
The 70-475 exam is part of the Azure Big Data Analytics Solutions certification. This exam measures your ability to accomplish the below tasks based on this technology: Big Data Analytics Solutions Please note that this exam on February 2017 was updated.
This certification exam is targeted towards data management professionals, data architects, data scientists, and data developers who design big data analytics solutions on Microsoft Azure. Candidates for this exam will have relevant work experience in big data analytics solutions designing and deployment strategy for an end-to-end solution, This is a list of covered topics:
- Ingest data for real-time processing
- Implement master data management solutions
- Troubleshoot data integration issues
- Design and provision compute clusters
- Design for data security
- Move, transform, and analyze data using MapReduce
- Create and monitor a data factory
- Deploy con configure SSIS solutions
Microsoft 070-475日本語 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Design big data analytics solution | - Design data storage strategy using Azure data services
|
| Secure and monitor big data solutions | - Data security and access control - Monitoring and troubleshooting data pipelines |
| Develop and optimize data pipelines | - Performance tuning and scaling considerations - Data ingestion and transformation workflows |
| Implement data processing solutions | - Stream processing implementation
|
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