PDF Version Demo70-475 - 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:
- Design for data security
- Deploy con configure SSIS solutions
- Ingest data for real-time processing
- Troubleshoot data integration issues
- Implement master data management solutions
- Create and monitor a data factory
- Move, transform, and analyze data using MapReduce
- Design and provision compute clusters
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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: Operationalize End-to-End Cloud Analytics Solutions | 25-30% | - Create and configure Azure Data Factory - Orchestrate data processing workflows - Design deployment strategies - Move, transform, and analyze data - Monitor and manage data pipelines |
| Topic 2: Design Big Data Real-Time Processing Solutions | 25-30% | - Design for Lambda architecture - Design and provision compute resources - Design for real-time processing - Ingest data for real-time processing |
| Topic 3: Design Machine Learning Solutions | 25-30% | - Select input/output data types - Create and manage experiments - Apply custom processing with R and Python - Publish machine learning web services - Determine pre-processing and training locations |
| Topic 4: Design Big Data Batch Processing and Interactive Solutions | 20-25% | - Ingest data for batch and interactive processing - Design and provision compute clusters - Design for batch processing - Design interactive queries for big data - Design for data security |
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