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Reference: https://www.microsoft.com/en-us/learning/exam-70-776.aspx
Microsoft 70-776 Exam Syllabus Topics:
| Section | Weight | Objectives |
| Topic 1: Design and Implement Analytics by Using Azure Data Lake | 25-30% | - Ingest data into Azure Data Lake Store
- 1. Copy and secure data
- 2. Implement authentication and access control
- 3. Create and configure Data Lake Store accounts
- 4. Tune performance and diagnostics
- Integrate Azure Data Lake with other services
- 1. Integrate with Azure Data Factory
- 2. Integrate with Data Catalog and Event Hubs
- 3. Connect with Azure SQL Data Warehouse
- 4. Integrate with HDInsight
- Manage Azure Data Lake Analytics
- 1. Monitor and troubleshoot jobs
- 2. Create and manage analytics accounts
- 3. Manage users and data sources
- 4. Optimize jobs and review historical execution
- Extend U-SQL programmability
- 1. Implement user-defined functions and operators
- 2. Share code and data assets
- 3. Perform federated queries
- 4. Integrate Python and R
- Extract and transform data using U-SQL
- 1. Use U-SQL data types and expressions
- 2. Manage catalogs and structured data
- 3. Generate output files
- 4. Perform joins and analytical operations
|
| Topic 2: Design and Implement Azure SQL Data Warehouse Solutions | 15-20% | - Query data in Azure SQL Data Warehouse
- 1. Manage statistics
- 2. Monitor query performance
- 3. Implement query labels
- 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. Design table geometry
- 2. Select distribution methods
- 3. Design columnstore indexes
- 4. Minimize data skew
|
| Topic 3: Design and Implement Cloud-Based Integration by Using Azure Data Factory | 15-20% | - Orchestrate data processing pipelines
- 1. Provision and run pipelines
- 2. Manage dependencies
- 3. Design schedules
- 4. Design end-to-end data flows
- Implement datasets and linked services
- 1. Implement availability and policies
- 2. Create datasets
- 3. Configure linked services
- Move, transform, and analyze data
- 1. Create activity types
- 2. Move data to and from SQL Data Warehouse
- 3. Copy data between environments
- 4. Extend processing with custom activities
- Monitor and manage Azure Data Factory
- 1. Use monitoring tools
- 2. Identify failures
- 3. Perform redeployments
- 4. Create alerts
|
| Topic 4: Design and Implement Complex Event Processing by Using Azure Stream Analytics | 15-20% | - Design and implement Azure Stream Analytics
- 1. Implement scoring models
- 2. Support continuous learning scenarios
- 3. Configure thresholds and alerts
- 4. Integrate Azure Machine Learning
- Query real-time data
- 1. Use Stream Analytics query language
- 2. Guarantee event delivery
- 3. Manage time windows
- 4. Use built-in functions and data types
- Implement and manage streaming pipelines
- 1. Coordinate stream and batch processing
- 2. Stream data to dashboards
- 3. Archive streaming data
- Ingest data for real-time processing
- 1. Estimate throughput and latency requirements
- 2. Design partitioning schemes
- 3. Select appropriate ingestion technologies
- 4. Process streaming data sources
- 5. Design reference data streams
|
| Topic 5: Manage and Maintain Azure SQL Data Warehouse, Azure Data Lake, Azure Data Factory, and Azure Stream Analytics | 20-25% | - Implement authentication, authorization, and auditing
- 1. Secure integrated services
- 2. Configure firewalls
- 3. Implement auditing
- 4. Integrate with Azure Active Directory
- Provision Azure services
- 1. Deploy Azure Data Lake
- 2. Deploy Azure SQL Data Warehouse
- 3. Deploy Azure Data Factory
- 4. Deploy Azure Stream Analytics
- Monitor and optimize services
- 1. Manage concurrency
- 2. Implement elastic scaling
- 3. Monitor workloads
- 4. Troubleshoot performance
- Manage data recovery
- 1. Implement geo-redundancy
- 2. Backup and recovery
- 3. Support migration scenarios
- Design storage solutions for big data
- 1. Optimize storage performance
- 2. Migrate data
- 3. Select storage technologies
- 4. Integrate cloud and on-premises solutions
|