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MICROSOFT 70-776 Exam Reference, Microsoft Documents

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Perform Big Data Engineering on Microsoft Cloud Services (beta) 70-776 Dumps Provided Study Notes

ITPassLeader expert team recommend you to prepare some notes on these topics along with it don't forget to practice Perform Big Data Engineering on Microsoft Cloud Services (beta) 70-776 Dumps which been written by our expert team.

Both these will help you a lot to clear this exam with good marks.

  • Azure Storage Explorer
  • Bulk Copy Program
  • Azure Event Hubs
  • T-SQL/U-SQL
  • Azure Stream Analytics
  • Azure Data Factory
  • Azure Data Factory Pipelines
  • Azure Data Warehouse
  • Azcopy
  • Azure Machine Learning
  • Azure Storage
  • SQL Server Integration Services
  • Azure Data Lake
  • Azure Backup Services

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

Microsoft 070-776 Exam Syllabus Topics:

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

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