Microsoft Engineering Data with Microsoft Cloud Services : 70-776 valid dump torrent

  • Exam Code: 70-776
  • Exam Name: Engineering Data with Microsoft Cloud Services
  • Updated: Sep 11, 2026
  • Q&As: 92 Questions and Answers

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About Microsoft Engineering Data with Microsoft Cloud Services : 70-776 Real Exam

To prepare for the Microsoft 070-776 certification exam, candidates must have a deep understanding of engineering data, data storage, and data processing concepts. They must also be familiar with various Microsoft cloud services and their use in engineering data processing. Microsoft offers a range of training courses and resources to help candidates prepare for the exam, including online training modules, study guides, and practice exams.

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Microsoft 070-776 certification exam is an essential requirement for professionals who work with engineering data and utilize Microsoft cloud services. Engineering Data with Microsoft Cloud Services certification validates professionals' skills and expertise in managing, analyzing, and processing engineering data using Microsoft cloud services. With the help of Microsoft's training resources, candidates can prepare and pass the exam to achieve their career goals and unlock new opportunities for growth and advancement.

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

Microsoft 70-776 Exam Syllabus Topics:

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

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