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Difficulty in writing MICROSOFT 70-776 Exam

It is one of the toughest exam and it requires you a lot of hands-on experience. It is advisable to have prior knowledge of Big Data analytics on Microsoft. The more experience you have, the more it is beneficial for you, Major challenging things which you can find in exam is about different scenario based questions, you must have strong understanding of Big Data, Machine Learning, Data Science, Data Analytics.

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

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

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Microsoft 070-776 Exam Syllabus Topics:

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

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