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Overview about MICROSOFT 70-776 Exam

Registration Fee: 165 USD
Format: Multiple choice, multiple answer
Number of Questions: 40-60
Passing Score: 70-80%
Length of Examination: 120 minutes

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Perform Big Data Engineering on Microsoft Cloud Services 70-776 Exam

Perform Big Data Engineering on Microsoft Cloud Services 70-776 Exam is intended mainly to design analytical solutions and build operational solutions on Azure. Candidates for this exam have relevant work experience in data engineering issues with Azure SQL Data Warehouse, Azure Data Lake, Azure Data Factory, and Azure Stream Analytics. Candidates are familiar with the features and capabilities of batch data processing, real-time processing, and operationalization technologies.

Candidates for this exam can apply the Microsoft cloud ecosystem to designs and can implement big data analytics solutions to meet their project's needs. In an effort to expand out the breadth of certifications and exams offered around Microsoft Azure and the cloud, Microsoft has added the Perform Big Data Engineering on Microsoft Cloud Services 70-776 Exam. This exam only available for the candidates till 30th June 2019 after that this may get replace with some other exam so you can plan get it completed before it get expire.

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Cloud based integration using Azure Data Factory.
migrate from an on-premises data warehouse to Azure SQL Data Warehouse
Azure Data Factory Pipelines.
Provision Azure SQL Data Warehouse, Azure Data Lake, Azure Data Factory, and Azure Stream Analytics
Backup and Recovery Service Plan for Azure Storage
Monitor Azure SQL Data Warehouse, Azure Data Lake, and Azure Stream Analytics
Design Storage Solutions

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

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