PDF Version DemoHigh Quality Of Design and Implement Big Data Analytics Solutions (070-475日本語版) Exam
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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:
- Deploy con configure SSIS solutions
- Create and monitor a data factory
- Implement master data management solutions
- Troubleshoot data integration issues
- Design and provision compute clusters
- Move, transform, and analyze data using MapReduce
- Ingest data for real-time processing
- Design for data security
Reference: https://www.microsoft.com/en-pk/learning/exam-70-475.aspx
Our 70-475 exam dumps will include those topics:
- Design big data real-time processing solutions (30-35%)
- Design big data batch processing and interactive solutions (30-35%)
- Operationalize end-to-end cloud analytics solutions (30-35%)
For more info visit: Microsoft Official 70-475 Exam Reference
High 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.
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Microsoft 070-475日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Design Big Data Batch Processing and Interactive Solutions | 20-25% | - Design for data security - Design interactive queries for big data - Design and provision compute clusters - Ingest data for batch and interactive processing - Design for batch processing |
| Design Machine Learning Solutions | 25-30% | - Apply custom processing with R and Python - Create and manage experiments - Select input/output data types - Determine pre-processing and training locations - Publish machine learning web services |
| Operationalize End-to-End Cloud Analytics Solutions | 25-30% | - Design deployment strategies - Create and configure Azure Data Factory - Orchestrate data processing workflows - Move, transform, and analyze data - Monitor and manage data pipelines |
| Design Big Data Real-Time Processing Solutions | 25-30% | - Design for Lambda architecture - Design for real-time processing - Design and provision compute resources - Ingest data for real-time processing |
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