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Overview about MICROSOFT 70-776 Exam

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

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Perform Big Data Engineering on Microsoft Cloud Services (beta) 70-776 Dumps will include below mentioned topics with Exam focused percentage

  • Design and Implement Cloud-Based Integration by using Azure Data Factory: 15-20%
  • Design and Implement Analytics by Using Azure Data Lake: 25-30%
  • Design and Implement Azure SQL Data Warehouse Solutions: 15-20%
  • Manage and Maintain Azure SQL Data Warehouse, Azure Data Lake, Azure Data Factory, and Azure Stream Analytics: 20-25%
  • Design and Implement Complex Event Processing By Using Azure Stream Analytics: 15-20%

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MICROSOFT 70-776 Exam Reference, Microsoft Documents

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

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