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

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Microsoft 070-776 Exam Syllabus Topics:

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

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