Microsoft 070-776 dumps - in .pdf

070-776 pdf
  • Exam Code: 070-776
  • Exam Name: Engineering Data with Microsoft Cloud Services
  • Updated: Aug 20, 2026
  • Q & A: 92 Questions and Answers
  • PDF Price: $59.99
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  • Exam Code: 070-776
  • Exam Name: Engineering Data with Microsoft Cloud Services
  • Updated: Aug 20, 2026
  • Q & A: 92 Questions and Answers
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070-776 Testing Engine
  • Exam Code: 070-776
  • Exam Name: Engineering Data with Microsoft Cloud Services
  • Updated: Aug 20, 2026
  • Q & A: 92 Questions and Answers
  • Software Price: $59.99
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Microsoft 070-776: Engineering Data with Microsoft Cloud Services is a certification exam that measures an individual's understanding and ability to leverage Microsoft Azure cloud services to support and manage engineering data. 070-776 exam is designed to validate the candidate's proficiency in designing and implementing data storage, data security, and data management solutions using Microsoft Azure cloud services.

Microsoft 070-776 exam is designed for professionals who have a strong understanding of data engineering principles and are interested in learning how to use Microsoft Cloud Services to manage and analyze large-scale data projects. Engineering Data with Microsoft Cloud Services certification exam is part of the Microsoft Certified Solutions Expert (MCSE) program, and passing it can help professionals advance their careers and demonstrate their expertise in the field of data engineering.

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The Microsoft 070-776 exam measures the candidate's proficiency in designing and implementing data solutions in Azure, managing and monitoring data processing, and implementing security and compliance policies. 070-776 exam also tests the candidate's ability to use various tools and services such as Azure Data Factory, Azure Data Lake, Azure Stream Analytics, and Azure SQL Data Warehouse. 070-776 exam is ideal for professionals who are involved in data engineering, data warehousing, and data analytics and want to validate their skills and knowledge in using Microsoft Cloud Services for these purposes.

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

Microsoft 070-776 Exam Syllabus Topics:

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

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