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Microsoft 070-776 Exam Syllabus Topics:

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