Microsoft Engineering Data with Microsoft Cloud Services : 070-776

070-776 pass collection

Exam Code: 070-776

Exam Name: Engineering Data with Microsoft Cloud Services

Updated: Oct 12, 2026

Q & A: 92 Questions and Answers

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

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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.

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

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Overview about MICROSOFT 70-776 Exam

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

Microsoft 070-776 Exam Syllabus Topics:

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

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