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Perform Big Data Engineering on Microsoft Cloud Services (beta) 70-776 Dumps will include below mentioned topics with Exam focused percentage

  • Design and Implement Azure SQL Data Warehouse Solutions: 15-20%
  • Manage and Maintain Azure SQL Data Warehouse, Azure Data Lake, Azure Data Factory, and Azure Stream Analytics: 20-25%
  • Design and Implement Analytics by Using Azure Data Lake: 25-30%
  • Design and Implement Cloud-Based Integration by using Azure Data Factory: 15-20%
  • Design and Implement Complex Event Processing By Using Azure Stream Analytics: 15-20%

How to book MICROSOFT 70-776 Certifications Exam?

  • Signup/Login to MICROSOFT account
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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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This is the list of the contents in our Perform Big Data Engineering on Microsoft Cloud Services 70-776 Practice Test:

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

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

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Microsoft 070-776 Exam Syllabus Topics:

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

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