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Microsoft Azure SQL Data Warehous 70-776 Braindumps

70-776

Exam Code: 70-776

Exam Name: Engineering Data with Microsoft Cloud Services

Updated: Oct 19, 2026

Q & A: 92 Questions and Answers

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About Microsoft 70-776 Real Exam Collection

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

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

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

Perform Big Data Engineering on Microsoft Cloud Services (beta) 70-776 Dumps will include below mentioned topics with Exam focused percentage

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

Microsoft 70-776 Exam Syllabus Topics:

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