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  • 070-776 (Engineering Data with Microsoft Cloud Services)
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

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Updated: Jul 24, 2026

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MICROSOFT 70-776 Exam Reference, Microsoft Documents

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

How to book MICROSOFT 70-776 Certifications Exam?

  • Signup/Login to MICROSOFT account
  • Visit to Microsoft Exam Registration
  • Select Date and Center of examination and confirm with payment value of 165$
  • Search for MICROSOFT 70-776 Certifications Exam

Microsoft 070-776 Exam Overview:

Certification Vendor:Microsoft
Exam Name:Engineering Data with Microsoft Cloud Services
Exam Number:70-776
Exam Price:USD 165
Real Exam Qty:40-60
Certificate Validity Period:Certification retired; exam retired June 30, 2019
Exam Format:Multiple Choice, Multiple Response, Case Study, Scenario-Based Questions, Build List, Drag and Drop
Related Certifications:MCSA: Data Engineering with Azure
MCSE: Data Management and Analytics
Passing Score:700/1000
Exam Duration:120-150
Available Languages:English
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/

Overview about MICROSOFT 70-776 Exam

  • Format: Multiple choice, multiple answer
  • Length of Examination: 120 minutes
  • Number of Questions: 40-60
  • Passing Score: 70-80%
  • Registration Fee: 165 USD

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

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

Microsoft 070-776 Exam Syllabus Topics:

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

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