Microsoft 070-776 Exam Questions : Engineering Data with Microsoft Cloud Services

  • Exam Code: 070-776
  • Exam Name: Engineering Data with Microsoft Cloud Services
  • Updated: Jul 18, 2026
  • Q&As: 92 Questions and Answers

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

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

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

For more info visit:

MICROSOFT 70-776 Exam Reference, Microsoft Documents

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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

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

Microsoft 070-776 Exam Syllabus Topics:

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

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