Perform Big Data Engineering on Microsoft Cloud Services (beta) 70-776 Dumps Provided Study Notes

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T-SQL/U-SQL
Azcopy
Azure Storage Explorer
Azure Data Lake
Bulk Copy Program
Azure Event Hubs
Azure Data Factory
Azure Machine Learning
Azure Data Factory Pipelines
Azure Data Warehouse
Azure Stream Analytics
Azure Storage
Azure Backup Services
SQL Server Integration Services

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

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Microsoft 70-776 Exam Syllabus Topics:

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