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The Microsoft 70-475J exam covers a range of topics, including designing big data batch processing solutions, designing big data real-time processing solutions, designing machine learning solutions, and designing big data storage solutions. Candidates also need to have knowledge of various tools and technologies used for big data analytics, including Hadoop, Spark, and Azure Data Lake. Passing the Microsoft 070-475 exam demonstrates that a candidate has the skills and knowledge required to design and implement big data solutions that are scalable, reliable, and efficient.
Reference: https://www.microsoft.com/en-pk/learning/exam-70-475.aspx
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Microsoft 070-475 (Design and Implement Big Data Analytics Solutions) Exam is designed for professionals who want to validate their skills in designing and implementing big data analytics solutions using Microsoft technologies. 70-475J exam measures the candidate's ability to design and implement big data solutions that can process and analyze large datasets to extract valuable insights and support business decisions. It also tests the candidate's proficiency in using Microsoft tools and services such as Azure HDInsight, Azure Stream Analytics, Azure Data Lake, and Azure Machine Learning to build big data analytics solutions.
Microsoft 70-475日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: 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 |
| Topic 2: Operationalize End-to-End Cloud Analytics Solutions | 25-30% | - Monitor and manage data pipelines - Design deployment strategies - Create and configure Azure Data Factory - Orchestrate data processing workflows - Move, transform, and analyze data |
| Topic 3: Design Big Data Batch Processing and Interactive Solutions | 20-25% | - Design interactive queries for big data - Ingest data for batch and interactive processing - Design for batch processing - Design for data security - Design and provision compute clusters |
| Topic 4: Design Machine Learning Solutions | 25-30% | - Determine pre-processing and training locations - Apply custom processing with R and Python - Select input/output data types - Publish machine learning web services - Create and manage experiments |
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