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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

Last Updated: Jul 26, 2026

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MICROSOFT 70-776 Exam Reference, Microsoft Documents

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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:

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

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

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

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

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Microsoft 070-776 Exam Syllabus Topics:

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

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