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Microsoft 070-776 : Engineering Data with Microsoft Cloud Services

070-776

Exam Code: 070-776

Exam Name: Engineering Data with Microsoft Cloud Services

Updated: Aug 11, 2026

Q & A: 92 Questions and Answers

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About Microsoft 070-776 Exam

Overview about MICROSOFT 70-776 Exam

  • Passing Score: 70-80%
  • Registration Fee: 165 USD
  • Number of Questions: 40-60
  • Length of Examination: 120 minutes
  • Format: Multiple choice, multiple answer

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MICROSOFT 70-776 Exam Reference, Microsoft Documents

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

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

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

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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.

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Microsoft 070-776 Exam Syllabus Topics:

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

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070-776 - Engineering Data with Microsoft Cloud Services
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