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Perform Big Data Engineering on Microsoft Cloud Services 70-776 Exam

Perform Big Data Engineering on Microsoft Cloud Services 70-776 Exam is intended mainly to design analytical solutions and build operational solutions on Azure. Candidates for this exam have relevant work experience in data engineering issues with Azure SQL Data Warehouse, Azure Data Lake, Azure Data Factory, and Azure Stream Analytics. Candidates are familiar with the features and capabilities of batch data processing, real-time processing, and operationalization technologies.

Candidates for this exam can apply the Microsoft cloud ecosystem to designs and can implement big data analytics solutions to meet their project's needs. In an effort to expand out the breadth of certifications and exams offered around Microsoft Azure and the cloud, Microsoft has added the Perform Big Data Engineering on Microsoft Cloud Services 70-776 Exam. This exam only available for the candidates till 30th June 2019 after that this may get replace with some other exam so you can plan get it completed before it get expire.

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  • Cloud based integration using Azure Data Factory.
  • Azure Data Factory Pipelines.
  • Monitor Azure SQL Data Warehouse, Azure Data Lake, and Azure Stream Analytics
  • Backup and Recovery Service Plan for Azure Storage
  • Design Storage Solutions
  • migrate from an on-premises data warehouse to Azure SQL Data Warehouse
  • Provision Azure SQL Data Warehouse, Azure Data Lake, Azure Data Factory, and Azure Stream Analytics

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Microsoft 70-776 Exam Syllabus Topics:

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

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