Microsoft 070-776 exam - in .pdf

070-776 pdf
  • Exam Code: 070-776
  • Exam Name: Engineering Data with Microsoft Cloud Services
  • Q & A: 92 Questions and Answers
  • PDF Price: $59.99
  • PDF Demo

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  • Exam Code: 070-776
  • Exam Name: Engineering Data with Microsoft Cloud Services
  • Q & A: 92 Questions and Answers
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Microsoft 070-776 exam - Testing Engine

070-776 Testing Engine
  • Exam Code: 070-776
  • Exam Name: Engineering Data with Microsoft Cloud Services
  • Q & A: 92 Questions and Answers
  • Software Price: $59.99
  • Testing Engine

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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 Analytics by Using Azure Data Lake: 25-30%
  • Design and Implement Azure SQL Data Warehouse Solutions: 15-20%
  • Design and Implement Complex Event Processing By Using Azure Stream Analytics: 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%

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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:

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

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Microsoft 070-776 Exam Syllabus Topics:

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

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