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

Pass4Leader 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.

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

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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.

We think our Perform Big Data Engineering on Microsoft Cloud Services (beta) 70-776 Exam Practice Test Paper and Dumps will provide you 100% confidence to make you appear for MICROSOFT 70-776 Exam.

This is the list of the contents in our Perform Big Data Engineering on Microsoft Cloud Services 70-776 Practice Test:

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

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

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Microsoft 70-776 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Design and Implement Analytics by Using Azure Data Lake25-30%- 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
- Manage Azure Data Lake Analytics
  • 1. Manage users and data sources
  • 2. Optimize jobs and review historical execution
  • 3. Create and manage analytics accounts
  • 4. Monitor and troubleshoot jobs
- Extend U-SQL programmability
  • 1. Share code and data assets
  • 2. Perform federated queries
  • 3. Implement user-defined functions and operators
  • 4. Integrate Python and R
- Extract and transform data using U-SQL
  • 1. Use U-SQL data types and expressions
  • 2. Generate output files
  • 3. Perform joins and analytical operations
  • 4. Manage catalogs and structured data
- Ingest data into Azure Data Lake Store
  • 1. Create and configure Data Lake Store accounts
  • 2. Copy and secure data
  • 3. Implement authentication and access control
  • 4. Tune performance and diagnostics
Topic 2: Design and Implement Complex Event Processing by Using Azure Stream Analytics15-20%- Design and implement Azure Stream Analytics
  • 1. Support continuous learning scenarios
  • 2. Implement scoring models
  • 3. Configure thresholds and alerts
  • 4. Integrate Azure Machine Learning
- Ingest data for real-time processing
  • 1. Estimate throughput and latency requirements
  • 2. Design partitioning schemes
  • 3. Select appropriate ingestion technologies
  • 4. Process streaming data sources
  • 5. Design reference data streams
- Implement and manage streaming pipelines
  • 1. Stream data to dashboards
  • 2. Coordinate stream and batch processing
  • 3. Archive streaming data
- Query real-time data
  • 1. Use built-in functions and data types
  • 2. Guarantee event delivery
  • 3. Use Stream Analytics query language
  • 4. Manage time windows
Topic 3: Design and Implement Azure SQL Data Warehouse Solutions15-20%- Query data in Azure SQL Data Warehouse
  • 1. Manage resource classes
  • 2. Implement query labels
  • 3. Monitor query performance
  • 4. Manage statistics
- 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
- Design tables in Azure SQL Data Warehouse
  • 1. Design table geometry
  • 2. Minimize data skew
  • 3. Design columnstore indexes
  • 4. Select distribution methods
Topic 4: Manage and Maintain Azure SQL Data Warehouse, Azure Data Lake, Azure Data Factory, and Azure Stream Analytics20-25%- Provision Azure services
  • 1. Deploy Azure Data Lake
  • 2. Deploy Azure SQL Data Warehouse
  • 3. Deploy Azure Stream Analytics
  • 4. Deploy Azure Data Factory
- Monitor and optimize services
  • 1. Monitor workloads
  • 2. Troubleshoot performance
  • 3. Manage concurrency
  • 4. Implement elastic scaling
- Implement authentication, authorization, and auditing
  • 1. Integrate with Azure Active Directory
  • 2. Configure firewalls
  • 3. Implement auditing
  • 4. Secure integrated services
- Design storage solutions for big data
  • 1. Migrate data
  • 2. Select storage technologies
  • 3. Integrate cloud and on-premises solutions
  • 4. Optimize storage performance
- Manage data recovery
  • 1. Support migration scenarios
  • 2. Implement geo-redundancy
  • 3. Backup and recovery
Topic 5: Design and Implement Cloud-Based Integration by Using Azure Data Factory15-20%- Monitor and manage Azure Data Factory
  • 1. Identify failures
  • 2. Perform redeployments
  • 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
- Implement datasets and linked services
  • 1. Create datasets
  • 2. Configure linked services
  • 3. Implement availability and policies
- Orchestrate data processing pipelines
  • 1. Provision and run pipelines
  • 2. Design schedules
  • 3. Design end-to-end data flows
  • 4. Manage dependencies

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