Engineering Data with Microsoft Cloud Services: 070-776 Exam

"Engineering Data with Microsoft Cloud Services", also known as 070-776 exam, is a Microsoft Certification. With the complete collection of questions and answers, PrepPDF has assembled to take you through 92 Q&As to your 070-776 Exam preparation. In the 070-776 exam resources, you will cover every field and category in Microsoft Azure SQL Data Warehous Certification helping to ready you for your successful Microsoft Certification.

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  • Exam Code: 070-776
  • Exam Name: Engineering Data with Microsoft Cloud Services
  • Certification Provider: Microsoft
  • Corresponding Certification: Microsoft Azure SQL Data Warehous
  • Updated: Jul 18, 2026
  • No. of Questions: 92 Questions & Answers with Testing Engine
  • Download Limit: Unlimited

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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.

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

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MICROSOFT 70-776 Exam Reference, Microsoft Documents

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

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Microsoft 070-776 Exam Syllabus Topics:

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

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