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

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Microsoft 070-776 Exam Syllabus Topics:

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