Microsoft Engineering Data with Microsoft Cloud Services : 070-776 valid dumps

070-776 real exams

Exam Code: 070-776

Exam Name: Engineering Data with Microsoft Cloud Services

Updated: Jul 17, 2026

Q & A: 92 Questions and Answers

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

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Both these will help you a lot to clear this exam with good marks.

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

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

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Microsoft 070-776 Exam Syllabus Topics:

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