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Microsoft 070-776 certification exam covers a wide range of topics, including data storage, data processing, data analysis, and data visualization. 70-776 exam also covers how to use Microsoft cloud services to manage big data solutions and how to integrate data solutions with other Microsoft services. Engineering Data with Microsoft Cloud Services certification exam is ideal for professionals who want to showcase their expertise in engineering data with Microsoft Cloud Services and want to advance their careers in the field.

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Microsoft 070-776 exam is intended for individuals who have a good understanding of data engineering and management concepts, as well as practical experience in using Microsoft cloud services. It is an associate-level certification exam, and passing it will demonstrate that you have the skills and knowledge required to work with Microsoft's cloud services for data engineering and management. Furthermore, this certification can significantly increase your employment opportunities and earning potential, as it is recognized worldwide and in high demand by employers.

Reference: https://www.microsoft.com/en-us/learning/exam-70-776.aspx

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The topics covered in the Microsoft 070-776 exam include designing and implementing data storage solutions, managing and maintaining data processing systems, and ensuring data security and compliance. 70-776 exam also covers topics such as data integration, data analytics, and data visualization.

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MICROSOFT 70-776 Exam Reference, Microsoft Documents

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Microsoft 70-776 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Design and Implement Azure SQL Data Warehouse Solutions15-20%- Integrate Azure SQL Data Warehouse with other services
  • 1. Import and export data
  • 2. Migrate enterprise data warehouses
  • 3. Use PolyBase and data ingestion tools
  • 4. Integrate with Azure Machine Learning
- Query data in Azure SQL Data Warehouse
  • 1. Implement query labels
  • 2. Manage statistics
  • 3. Manage resource classes
  • 4. Monitor query performance
- Design tables in Azure SQL Data Warehouse
  • 1. Design columnstore indexes
  • 2. Design table geometry
  • 3. Select distribution methods
  • 4. Minimize data skew
Topic 2: Design and Implement Cloud-Based Integration by Using Azure Data Factory15-20%- Monitor and manage Azure Data Factory
  • 1. Identify failures
  • 2. Use monitoring tools
  • 3. Perform redeployments
  • 4. Create alerts
- Implement datasets and linked services
  • 1. Implement availability and policies
  • 2. Create datasets
  • 3. Configure linked services
- Orchestrate data processing pipelines
  • 1. Manage dependencies
  • 2. Provision and run pipelines
  • 3. Design end-to-end data flows
  • 4. Design schedules
- Move, transform, and analyze data
  • 1. Extend processing with custom activities
  • 2. Move data to and from SQL Data Warehouse
  • 3. Copy data between environments
  • 4. Create activity types
Topic 3: Design and Implement Analytics by Using Azure Data Lake25-30%- Ingest data into Azure Data Lake Store
  • 1. Copy and secure data
  • 2. Tune performance and diagnostics
  • 3. Implement authentication and access control
  • 4. Create and configure Data Lake Store accounts
- Extend U-SQL programmability
  • 1. Perform federated queries
  • 2. Share code and data assets
  • 3. Integrate Python and R
  • 4. Implement user-defined functions and operators
- Manage Azure Data Lake Analytics
  • 1. Create and manage analytics accounts
  • 2. Optimize jobs and review historical execution
  • 3. Manage users and data sources
  • 4. Monitor and troubleshoot jobs
- Integrate Azure Data Lake with other services
  • 1. Integrate with HDInsight
  • 2. Connect with Azure SQL Data Warehouse
  • 3. Integrate with Azure Data Factory
  • 4. Integrate with Data Catalog and Event Hubs
- Extract and transform data using U-SQL
  • 1. Manage catalogs and structured data
  • 2. Perform joins and analytical operations
  • 3. Generate output files
  • 4. Use U-SQL data types and expressions
Topic 4: Design and Implement Complex Event Processing by Using Azure Stream Analytics15-20%- Query real-time data
  • 1. Manage time windows
  • 2. Guarantee event delivery
  • 3. Use built-in functions and data types
  • 4. Use Stream Analytics query language
- Ingest data for real-time processing
  • 1. Design reference data streams
  • 2. Select appropriate ingestion technologies
  • 3. Estimate throughput and latency requirements
  • 4. Design partitioning schemes
  • 5. Process streaming data sources
- Design and implement Azure Stream Analytics
  • 1. Integrate Azure Machine Learning
  • 2. Configure thresholds and alerts
  • 3. Implement scoring models
  • 4. Support continuous learning scenarios
- Implement and manage streaming pipelines
  • 1. Archive streaming data
  • 2. Coordinate stream and batch processing
  • 3. Stream data to dashboards
Topic 5: Manage and Maintain Azure SQL Data Warehouse, Azure Data Lake, Azure Data Factory, and Azure Stream Analytics20-25%- Manage data recovery
  • 1. Support migration scenarios
  • 2. Implement geo-redundancy
  • 3. Backup and recovery
- Design storage solutions for big data
  • 1. Integrate cloud and on-premises solutions
  • 2. Select storage technologies
  • 3. Migrate data
  • 4. Optimize storage performance
- Provision Azure services
  • 1. Deploy Azure Data Lake
  • 2. Deploy Azure Data Factory
  • 3. Deploy Azure SQL Data Warehouse
  • 4. Deploy Azure Stream Analytics
- Monitor and optimize services
  • 1. Implement elastic scaling
  • 2. Monitor workloads
  • 3. Troubleshoot performance
  • 4. Manage concurrency
- Implement authentication, authorization, and auditing
  • 1. Integrate with Azure Active Directory
  • 2. Implement auditing
  • 3. Configure firewalls
  • 4. Secure integrated services

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