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Microsoft 70-776 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Design and Implement Azure SQL Data Warehouse Solutions	15-20%	- Query data in Azure SQL Data Warehouse 1. Manage resource classes 2. Implement query labels 3. Monitor query performance 4. Manage statistics - Integrate Azure SQL Data Warehouse with other services 1. Use PolyBase and data ingestion tools 2. Migrate enterprise data warehouses 3. Integrate with Azure Machine Learning 4. Import and export data - Design tables in Azure SQL Data Warehouse 1. Design table geometry 2. Select distribution methods 3. Design columnstore indexes 4. Minimize data skew
Topic 2: Design and Implement Cloud-Based Integration by Using Azure Data Factory	15-20%	- 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. Create alerts 3. Identify failures 4. Use monitoring tools - Implement datasets and linked services 1. Implement availability and policies 2. Create datasets 3. Configure linked services - Move, transform, and analyze data 1. Move data to and from SQL Data Warehouse 2. Create activity types 3. Extend processing with custom activities 4. Copy data between environments
Topic 3: Design and Implement Analytics by Using Azure Data Lake	25-30%	- 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 - Integrate Azure Data Lake with other services 1. Integrate with HDInsight 2. Integrate with Azure Data Factory 3. Connect with Azure SQL Data Warehouse 4. Integrate with Data Catalog and Event Hubs - Ingest data into Azure Data Lake Store 1. Tune performance and diagnostics 2. Copy and secure data 3. Implement authentication and access control 4. Create and configure Data Lake Store accounts - 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 - Extract and transform data using U-SQL 1. Perform joins and analytical operations 2. Use U-SQL data types and expressions 3. Generate output files 4. Manage catalogs and structured data
Topic 4: Manage and Maintain Azure SQL Data Warehouse, Azure Data Lake, Azure Data Factory, and Azure Stream Analytics	20-25%	- Manage data recovery 1. Backup and recovery 2. Implement geo-redundancy 3. Support migration scenarios - Provision Azure services 1. Deploy Azure Data Lake 2. Deploy Azure Data Factory 3. Deploy Azure SQL Data Warehouse 4. Deploy Azure Stream Analytics - Design storage solutions for big data 1. Integrate cloud and on-premises solutions 2. Select storage technologies 3. Optimize storage performance 4. Migrate data - Implement authentication, authorization, and auditing 1. Secure integrated services 2. Configure firewalls 3. Implement auditing 4. Integrate with Azure Active Directory - Monitor and optimize services 1. Manage concurrency 2. Troubleshoot performance 3. Monitor workloads 4. Implement elastic scaling
Topic 5: Design and Implement Complex Event Processing by Using Azure Stream Analytics	15-20%	- 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 - Implement and manage streaming pipelines 1. Stream data to dashboards 2. Archive streaming data 3. Coordinate stream and batch processing - Query real-time data 1. Use Stream Analytics query language 2. Use built-in functions and data types 3. Guarantee event delivery 4. Manage time windows - Ingest data for real-time processing 1. Design reference data streams 2. Process streaming data sources 3. Design partitioning schemes 4. Select appropriate ingestion technologies 5. Estimate throughput and latency requirements