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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

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070-776

Updated: Aug 18, 2026

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Microsoft 070-776 Exam Overview:

Certification Vendor:Microsoft
Exam Name:Engineering Data with Microsoft Cloud Services
Exam Number:70-776
Related Certifications:MCSA: Data Engineering with Azure
MCSE: Data Management and Analytics
Passing Score:700/1000
Available Languages:English
Exam Duration:120-150
Exam Format:Multiple Choice, Multiple Response, Case Study, Scenario-Based Questions, Build List, Drag and Drop
Certificate Validity Period:Certification retired; exam retired June 30, 2019
Exam Price:USD 165
Real Exam Qty:40-60
Sample Questions:Microsoft 070-776 Sample Questions
Exam Way:Pearson VUE testing centers worldwide; retired June 30, 2019.
Pre Condition:No mandatory prerequisite exam. Intended for data engineers, data architects, data scientists, and data developers. Common prerequisites included Azure data services, relational databases, Windows administration, and data analytics experience.
Official Syllabus URL:https://learn.microsoft.com/en-us/credentials/

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:

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

Benefits of having MICROSOFT 70-776 Certifications

MICROSOFT 70-776 certification will not only help you learn some new skills, it can position you for a higher paying job or help you transform your current role into a Big Data and Analytics professional. Get prepared to take this exam and become one of the very first Microsoft Big Data Certified professionals in the world using our relevance quality of MICROSOFT 70-776 Exam study material and so we bring best-in-industry MICROSOFT 70-776 Exam online course and practice exams for you to help in your exam preparation.

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

Microsoft 070-776 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. Create and configure Data Lake Store accounts
  • 3. Tune performance and diagnostics
  • 4. Implement authentication and access control
- Manage Azure Data Lake Analytics
  • 1. Manage users and data sources
  • 2. Optimize jobs and review historical execution
  • 3. Create and manage analytics accounts
  • 4. Monitor and troubleshoot jobs
- Extend U-SQL programmability
  • 1. Share code and data assets
  • 2. Implement user-defined functions and operators
  • 3. Perform federated queries
  • 4. Integrate Python and R
- Integrate Azure Data Lake with other services
  • 1. Integrate with Data Catalog and Event Hubs
  • 2. Integrate with HDInsight
  • 3. Integrate with Azure Data Factory
  • 4. Connect with Azure SQL Data Warehouse
- Extract and transform data using U-SQL
  • 1. Generate output files
  • 2. Manage catalogs and structured data
  • 3. Perform joins and analytical operations
  • 4. Use U-SQL data types and expressions
Topic 2: Design and Implement Cloud-Based Integration by Using Azure Data Factory15-20%- Implement datasets and linked services
  • 1. Configure linked services
  • 2. Create datasets
  • 3. Implement availability and policies
- Move, transform, and analyze data
  • 1. Move data to and from SQL Data Warehouse
  • 2. Copy data between environments
  • 3. Extend processing with custom activities
  • 4. Create activity types
- Orchestrate data processing pipelines
  • 1. Design end-to-end data flows
  • 2. Manage dependencies
  • 3. Provision and run pipelines
  • 4. Design schedules
- Monitor and manage Azure Data Factory
  • 1. Create alerts
  • 2. Perform redeployments
  • 3. Use monitoring tools
  • 4. Identify failures
Topic 3: 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. Implement geo-redundancy
  • 3. Backup and recovery
- Design storage solutions for big data
  • 1. Select storage technologies
  • 2. Migrate data
  • 3. Optimize storage performance
  • 4. Integrate cloud and on-premises solutions
- Implement authentication, authorization, and auditing
  • 1. Integrate with Azure Active Directory
  • 2. Secure integrated services
  • 3. Configure firewalls
  • 4. Implement auditing
- Provision Azure services
  • 1. Deploy Azure Stream Analytics
  • 2. Deploy Azure Data Lake
  • 3. Deploy Azure SQL Data Warehouse
  • 4. Deploy Azure Data Factory
- Monitor and optimize services
  • 1. Manage concurrency
  • 2. Troubleshoot performance
  • 3. Implement elastic scaling
  • 4. Monitor workloads
Topic 4: Design and Implement Complex Event Processing by Using Azure Stream Analytics15-20%- Design and implement Azure Stream Analytics
  • 1. Configure thresholds and alerts
  • 2. Implement scoring models
  • 3. Integrate Azure Machine Learning
  • 4. Support continuous learning scenarios
- Query real-time data
  • 1. Use built-in functions and data types
  • 2. Manage time windows
  • 3. Use Stream Analytics query language
  • 4. Guarantee event delivery
- 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. Select appropriate ingestion technologies
  • 3. Design partitioning schemes
  • 4. Process streaming data sources
  • 5. Estimate throughput and latency requirements
Topic 5: Design and Implement Azure SQL Data Warehouse Solutions15-20%- Integrate Azure SQL Data Warehouse with other services
  • 1. Import and export data
  • 2. Integrate with Azure Machine Learning
  • 3. Use PolyBase and data ingestion tools
  • 4. Migrate enterprise data warehouses
- Query data in Azure SQL Data Warehouse
  • 1. Implement query labels
  • 2. Manage statistics
  • 3. Manage resource classes
  • 4. Monitor query performance
- Design tables in Azure SQL Data Warehouse
  • 1. Minimize data skew
  • 2. Design columnstore indexes
  • 3. Design table geometry
  • 4. Select distribution methods

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