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

  • Exam Code: 070-776
  • Exam Name: Engineering Data with Microsoft Cloud Services
  • Updated: Aug 06, 2026
  • Q & A: 92 Questions and Answers

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

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MICROSOFT 70-776 Exam Reference, Microsoft Documents

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Perform Big Data Engineering on Microsoft Cloud Services 70-776 Exam

Perform Big Data Engineering on Microsoft Cloud Services 70-776 Exam is intended mainly to design analytical solutions and build operational solutions on Azure. Candidates for this exam have relevant work experience in data engineering issues with Azure SQL Data Warehouse, Azure Data Lake, Azure Data Factory, and Azure Stream Analytics. Candidates are familiar with the features and capabilities of batch data processing, real-time processing, and operationalization technologies.

Candidates for this exam can apply the Microsoft cloud ecosystem to designs and can implement big data analytics solutions to meet their project's needs. In an effort to expand out the breadth of certifications and exams offered around Microsoft Azure and the cloud, Microsoft has added the Perform Big Data Engineering on Microsoft Cloud Services 70-776 Exam. This exam only available for the candidates till 30th June 2019 after that this may get replace with some other exam so you can plan get it completed before it get expire.

We think our Perform Big Data Engineering on Microsoft Cloud Services (beta) 70-776 Exam Practice Test Paper and Dumps will provide you 100% confidence to make you appear for MICROSOFT 70-776 Exam.

This is the list of the contents in our Perform Big Data Engineering on Microsoft Cloud Services 70-776 Practice Test:

  • Provision Azure SQL Data Warehouse, Azure Data Lake, Azure Data Factory, and Azure Stream Analytics
  • Design Storage Solutions
  • Monitor Azure SQL Data Warehouse, Azure Data Lake, and Azure Stream Analytics
  • migrate from an on-premises data warehouse to Azure SQL Data Warehouse
  • Cloud based integration using Azure Data Factory.
  • Backup and Recovery Service Plan for Azure Storage
  • Azure Data Factory Pipelines.

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

Microsoft 070-776 Exam Syllabus Topics:

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

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