Microsoft 070-776 Dumps : Engineering Data with Microsoft Cloud Services

070-776 real exams

Exam Code: 070-776

Exam Name: Engineering Data with Microsoft Cloud Services

Updated: Sep 05, 2026

Q & A: 92 Questions and Answers

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

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