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Our 70-475 exam dumps will include those topics:
- Design big data batch processing and interactive solutions (30-35%)
- Design big data real-time processing solutions (30-35%)
- Operationalize end-to-end cloud analytics solutions (30-35%)
For more info visit: Microsoft Official 70-475 Exam Reference
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Microsoft 070-475 exam covers a range of topics related to big data analytics, including designing big data batch processing and real-time processing solutions, implementing Azure HDInsight clusters, designing and implementing a data warehouse, and designing and implementing data security. 070-475J exam also covers topics related to data ingestion, data storage, data processing and analysis, and data visualization. Test-takers must have a deep understanding of the technologies and concepts involved in the field of big data analytics in order to pass the Microsoft 070-475 exam.
Reference: https://www.microsoft.com/en-pk/learning/exam-70-475.aspx
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In 070-475J exam, the candidates are required to demonstrate their knowledge and skills in various areas of big data analytics, such as designing and implementing big data solutions, implementing security and compliance, and managing and maintaining big data solutions. 070-475J exam also covers topics such as data analysis and visualization, data integration, data processing, and data storage. The candidates are expected to have a good understanding of a variety of big data technologies, including Hadoop, Spark, HBase, and Hive. Additionally, they should also be familiar with various data analytics tools and techniques, such as Power BI and R.
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Microsoft 070-475日本語 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Implement data processing solutions | - Batch processing with distributed computing
|
| Topic 2: Secure and monitor big data solutions | - Data security and access control - Monitoring and troubleshooting data pipelines |
| Topic 3: Design big data analytics solution | - Design data storage strategy using Azure data services
|
| Topic 4: Develop and optimize data pipelines | - Data ingestion and transformation workflows - Performance tuning and scaling considerations |
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