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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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Updated: Sep 06, 2026

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The Microsoft 070-776 exam measures the candidate's ability to implement cloud-based solutions for managing and processing engineering data using Microsoft Azure services. It covers various topics, including designing and implementing data storage solutions, implementing data processing solutions, managing and monitoring data solutions, and securing data solutions. 070-776 exam also evaluates the candidate's knowledge of Azure Storage, Azure Data Lake, Azure Stream Analytics, and other relevant technologies.

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

Microsoft 070-776 Exam Overview:

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

Microsoft 070-776 exam is intended for IT professionals with experience in designing and implementing data solutions using Microsoft Azure. This includes data engineers, database administrators, data scientists, and other IT professionals who work with big data and analytics. To be eligible to take the exam, candidates must have a good understanding of the Microsoft Azure platform and be proficient in programming languages like Python, R, and PowerShell.

Microsoft 070-776 Exam Syllabus Topics:

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

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