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Design and Implement Big Data Analytics Solutions (70-475日本語版): 70-475 Exam
"Design and Implement Big Data Analytics Solutions (70-475日本語版)", also known as 70-475 exam, is a Microsoft Certification. With the complete collection of questions and answers, PrepAwayTest has assembled to take you through 122 Q&As to your 70-475 Exam preparation. In the 70-475 exam resources, you will cover every field and category in Microsoft Azure Certification helping to ready you for your successful Microsoft Certification.
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The Design and Implement Big Data Analytics Solutions (70-475日本語版) certification is beneficial for individuals who are interested in a career in big data analytics, as it validates their knowledge and skills in this field. It can also help professionals who are currently working in this field to advance their career and to add value to their employer. It is a great way for IT professionals to differentiate themselves in the competitive job market, and to demonstrate their commitment to furthering their knowledge and skills.
Reference: https://www.microsoft.com/en-pk/learning/exam-70-475.aspx
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Microsoft 70-475日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Operationalize End-to-End Cloud Analytics Solutions | 25-30% | - Create and configure Azure Data Factory - Design deployment strategies - Monitor and manage data pipelines - Move, transform, and analyze data - Orchestrate data processing workflows |
| Design Machine Learning Solutions | 25-30% | - Create and manage experiments - Publish machine learning web services - Determine pre-processing and training locations - Apply custom processing with R and Python - Select input/output data types |
| Design Big Data Batch Processing and Interactive Solutions | 20-25% | - Ingest data for batch and interactive processing - Design interactive queries for big data - Design and provision compute clusters - Design for batch processing - Design for data security |
| Design Big Data Real-Time Processing Solutions | 25-30% | - Design and provision compute resources - Design for Lambda architecture - Design for real-time processing - Ingest data for real-time processing |
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