Microsoft 70-776 exam dumps - Engineering Data with Microsoft Cloud Services

  • Exam Code: 70-776
  • Exam Name: Engineering Data with Microsoft Cloud Services
  • Updated: Jul 31, 2026     Q & A: 92 Questions and Answers

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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:

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

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70-776 exam dumps

Difficulty in writing MICROSOFT 70-776 Exam

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.

Candidates having thorough study and hands-on practice can help you to get prepare for this exam. It is all up to your decision we mean to say a source which you used for 70-776 exam preparation it may be a book or an online source which offered you 70-776. In these days people mostly prefer to buy their study material from an online platform and there are many online websites who are offering 70-776 test questions but they are not verified by experts. So, you have to choose a platform which gives you the best & authentic Microsoft 70-776 practice test paper & Microsoft 70-776 exam dumps and i.e. only you can have it at FreeDumps because all their exams are verified by the Subject Matter Expert.

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

Microsoft 70-776 Exam Syllabus Topics:

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

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