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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.

We think our Perform Big Data Engineering on Microsoft Cloud Services (beta) 70-776 Exam Practice Test Paper and Dumps will provide you 100% confidence to make you appear for MICROSOFT 70-776 Exam.

This is the list of the contents in our Perform Big Data Engineering on Microsoft Cloud Services 70-776 Practice Test:

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

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How to book MICROSOFT 70-776 Certifications Exam?

  • Select Date and Center of examination and confirm with payment value of 165$
  • Signup/Login to MICROSOFT account
  • Visit to Microsoft Exam Registration
  • Search for MICROSOFT 70-776 Certifications Exam

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

Microsoft 070-776 Exam Syllabus Topics:

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

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