Microsoft 070-776 : Engineering Data with Microsoft Cloud Services

  • Exam Code: 070-776
  • Exam Name: Engineering Data with Microsoft Cloud Services
  • Updated: Aug 09, 2026   Q&As: 92 Questions and Answers

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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

  • 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%
  • Design and Implement Analytics by Using Azure Data Lake: 25-30%
  • Design and Implement Complex Event Processing By Using Azure Stream Analytics: 15-20%
  • Design and Implement Azure SQL Data Warehouse Solutions: 15-20%

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Perform Big Data Engineering on Microsoft Cloud Services (beta) 70-776 Dumps Provided Study Notes

TorrentVCE expert team recommend you to prepare some notes on these topics along with it don't forget to practice Perform Big Data Engineering on Microsoft Cloud Services (beta) 70-776 Dumps which been written by our expert team.

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

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

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

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