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Exam Code: 070-776
Exam Name: Engineering Data with Microsoft Cloud Services
Certification Provider: Microsoft
Corresponding Certification: Microsoft Azure SQL Data Warehous

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070-776

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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
  • Monitor Azure SQL Data Warehouse, Azure Data Lake, and Azure Stream Analytics
  • Design Storage Solutions
  • Provision Azure SQL Data Warehouse, Azure Data Lake, Azure Data Factory, and Azure Stream Analytics
  • migrate from an on-premises data warehouse to Azure SQL Data Warehouse

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

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

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

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Microsoft 070-776 Exam Syllabus Topics:

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

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