Engineering Data with Microsoft Cloud Services: 070-776 Exam

"Engineering Data with Microsoft Cloud Services", also known as 070-776 exam, is a Microsoft Certification. With the complete collection of questions and answers, ExamcollectionPass has assembled to take you through 92 Q&As to your 070-776 Exam preparation. In the 070-776 exam resources, you will cover every field and category in Microsoft Azure SQL Data Warehous Certification helping to ready you for your successful Microsoft Certification.

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  • Exam Code: 070-776
  • Exam Name: Engineering Data with Microsoft Cloud Services
  • Certification Provider: Microsoft
  • Corresponding Certification: Microsoft Azure SQL Data Warehous
  • Updated: Sep 10, 2026
  • No. of Questions: 92 Questions & Answers with Testing Engine
  • Download Limit: Unlimited

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It is one of the toughest exam and it requires you a lot of hands-on experience. It is advisable to have prior knowledge of Big Data analytics on Microsoft. The more experience you have, the more it is beneficial for you, Major challenging things which you can find in exam is about different scenario based questions, you must have strong understanding of Big Data, Machine Learning, Data Science, Data Analytics.

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

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

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Benefits of having MICROSOFT 70-776 Certifications

MICROSOFT 70-776 certification will not only help you learn some new skills, it can position you for a higher paying job or help you transform your current role into a Big Data and Analytics professional. Get prepared to take this exam and become one of the very first Microsoft Big Data Certified professionals in the world using our relevance quality of MICROSOFT 70-776 Exam study material and so we bring best-in-industry MICROSOFT 70-776 Exam online course and practice exams for you to help in your exam preparation.

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Microsoft 070-776 Exam Syllabus Topics:

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

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