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  • Total Questions: 92
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Overview about MICROSOFT 70-776 Exam

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

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

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MICROSOFT 70-776 Exam Reference, Microsoft Documents

Microsoft 70-776 Exam Syllabus Topics:

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

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