Microsoft Engineering Data with Microsoft Cloud Services : 070-776

  • Exam Code: Microsoft
  • Exam Name: Engineering Data with Microsoft Cloud Services
  • Updated: Sep 22, 2026
  • Q & A: 92 Questions and Answers

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  • Azure Storage Explorer
  • Bulk Copy Program
  • Azure Data Lake
  • T-SQL/U-SQL
  • Azure Machine Learning
  • Azure Event Hubs
  • Azcopy
  • Azure Data Factory Pipelines
  • Azure Data Factory
  • SQL Server Integration Services
  • Azure Backup Services
  • Azure Storage
  • Azure Stream Analytics
  • Azure Data Warehouse
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MICROSOFT 70-776 Exam Reference, Microsoft Documents

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

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