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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
- Move, transform, and analyze data using MapReduce
- Deploy con configure SSIS solutions
- Design and provision compute clusters
- Create and monitor a data factory
- Design for data security
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Our 70-475 exam dumps will include those topics:
- Design big data batch processing and interactive solutions (30-35%)
- Design big data real-time processing solutions (30-35%)
- Operationalize end-to-end cloud analytics solutions (30-35%)
For more info visit: Microsoft Official 70-475 Exam Reference
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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.
Reference: https://www.microsoft.com/en-pk/learning/exam-70-475.aspx
Microsoft 70-475日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Design Big Data Batch Processing and Interactive Solutions | 20-25% | - Design for batch processing - Design for data security - Ingest data for batch and interactive processing - Design and provision compute clusters - Design interactive queries for big data |
| Topic 2: Design Big Data Real-Time Processing Solutions | 25-30% | - Ingest data for real-time processing - Design for real-time processing - Design and provision compute resources - Design for Lambda architecture |
| Topic 3: Design Machine Learning Solutions | 25-30% | - Apply custom processing with R and Python - Publish machine learning web services - Select input/output data types - Determine pre-processing and training locations - Create and manage experiments |
| Topic 4: Operationalize End-to-End Cloud Analytics Solutions | 25-30% | - Orchestrate data processing workflows - Design deployment strategies - Move, transform, and analyze data - Create and configure Azure Data Factory - Monitor and manage data pipelines |
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