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