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  • Exam Code: 070-776
  • Exam Name: Engineering Data with Microsoft Cloud Services
  • Updated: Sep 24, 2026
  • Q & A: 92 Questions and Answers
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  • Exam Name: Engineering Data with Microsoft Cloud Services
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Microsoft 070-776 Exam Syllabus Topics:

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

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