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Microsoft 70-776 Valid Braindumps - Engineering Data with Microsoft Cloud Services

70-776
  • Exam Code: 70-776
  • Exam Name: Engineering Data with Microsoft Cloud Services
  • Updated: Sep 08, 2026
  • Q & A: 92 Questions and Answers

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About Microsoft 70-776 Exam

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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:

  • Monitor Azure SQL Data Warehouse, Azure Data Lake, and Azure Stream Analytics
  • Design Storage Solutions
  • Backup and Recovery Service Plan for Azure Storage
  • 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.

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

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How to book MICROSOFT 70-776 Certifications Exam?

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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%
  • Manage and Maintain Azure SQL Data Warehouse, Azure Data Lake, Azure Data Factory, and Azure Stream Analytics: 20-25%
  • 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%

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Microsoft 70-776 Exam Syllabus Topics:

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

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