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

  • Exam Code: 070-776
  • Exam Name: Engineering Data with Microsoft Cloud Services
  • Updated: Aug 06, 2026
  • Q & A: 92 Questions and Answers

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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

  • 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%
  • Design and Implement Cloud-Based Integration by using Azure Data Factory: 15-20%

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MICROSOFT 70-776 certification will not only help you learn some new skills, it can position you for a higher paying job or help you transform your current role into a Big Data and Analytics professional. Get prepared to take this exam and become one of the very first Microsoft Big Data Certified professionals in the world using our relevance quality of MICROSOFT 70-776 Exam study material and so we bring best-in-industry MICROSOFT 70-776 Exam online course and practice exams for you to help in your exam preparation.

Overview about MICROSOFT 70-776 Exam

  • Passing Score: 70-80%
  • Length of Examination: 120 minutes
  • Format: Multiple choice, multiple answer
  • Number of Questions: 40-60
  • Registration Fee: 165 USD

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

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