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Microsoft 070-475 certification exam is designed to test the skills and knowledge of professionals in the field of big data analytics. Design and Implement Big Data Analytics Solutions (070-475日本語版) certification is intended for individuals who are responsible for designing and implementing big data analytics solutions for organizations. 070-475J exam focuses on topics such as data processing, data storage, data analysis, and data visualization.
To take the Microsoft 070-475 exam, candidates should have experience designing and implementing big data solutions using Microsoft technologies. They should also have experience working with Microsoft Azure and be familiar with Microsoft Power BI. Candidates should also have a deep understanding of big data analytics technologies and be able to apply this knowledge to design and implement effective big data solutions.
Reference: https://www.microsoft.com/en-pk/learning/exam-70-475.aspx
The Microsoft 070-475J exam consists of multiple-choice questions, and candidates have three hours to complete it. 070-475J exam covers a wide range of topics related to big data analytics, including designing and implementing big data batch processing solutions, designing and implementing big data real-time processing solutions, designing and implementing big data interactive solutions, and designing and implementing big data storage solutions. To pass the exam, candidates must score at least 700 out of 1000.
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Microsoft 070-475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 - Monitor and manage data pipelines - Design deployment strategies - Move, transform, and analyze data - Orchestrate data processing workflows |
| Topic 2: Design Big Data Real-Time Processing Solutions | 25-30% | - Design and provision compute resources - Design for real-time processing - Ingest data for real-time processing - Design for Lambda architecture |
| Topic 3: Design Big Data Batch Processing and Interactive Solutions | 20-25% | - Design interactive queries for big data - Design and provision compute clusters - Ingest data for batch and interactive processing - Design for batch processing - Design for data security |
| Topic 4: Design Machine Learning Solutions | 25-30% | - Create and manage experiments - Determine pre-processing and training locations - Apply custom processing with R and Python - Select input/output data types - Publish machine learning web services |
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