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

Actual4Exams expert team recommend you to prepare some notes on these topics along with it don't forget to practice Perform Big Data Engineering on Microsoft Cloud Services (beta) 70-776 Dumps which been written by our expert team.

Both these will help you a lot to clear this exam with good marks.

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

For more info visit:

MICROSOFT 70-776 Exam Reference, Microsoft Documents

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

Microsoft 070-776 Exam Syllabus Topics:

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

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