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

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

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Perform Big Data Engineering on Microsoft Cloud Services (beta) 70-776 Dumps will include below mentioned topics with Exam focused percentage

Design and Implement Complex Event Processing By Using Azure Stream Analytics: 15-20%
Design and Implement Azure SQL Data Warehouse Solutions: 15-20%
Design and Implement Cloud-Based Integration by using Azure Data Factory: 15-20%
Manage and Maintain Azure SQL Data Warehouse, Azure Data Lake, Azure Data Factory, and Azure Stream Analytics: 20-25%
Design and Implement Analytics by Using Azure Data Lake: 25-30%

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Microsoft 070-776 Exam Syllabus Topics:

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

Microsoft 070-776 : Engineering Data with Microsoft Cloud Services

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

2.Will you fulfill our promise to refund if they fail Microsoft Azure SQL Data Warehous exam with our products?

Perform Big Data Engineering on Microsoft Cloud Services (beta) 70-776 Dumps will include below mentioned topics with Exam focused percentage

1.Is your 070-776 test online valid?

3.Why other companies' test questions are more (less) than yours?

4.When will release new version?

5.How to choose 070-776 test engine or 070-776 online test engine?

Microsoft 070-776 Exam Syllabus Topics:

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