Microsoft 070-776 dumps - in .pdf

070-776 pdf
  • Exam Code: 070-776
  • Exam Name: Engineering Data with Microsoft Cloud Services
  • Updated: Aug 20, 2026
  • Q & A: 92 Questions and Answers
  • PDF Price: $59.99
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  • Exam Code: 070-776
  • Exam Name: Engineering Data with Microsoft Cloud Services
  • Updated: Aug 20, 2026
  • Q & A: 92 Questions and Answers
  • PDF Version + PC Test Engine + Online Test Engine
  • Value Pack Total: $119.98  $79.99
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Microsoft 070-776 dumps - Testing Engine

070-776 Testing Engine
  • Exam Code: 070-776
  • Exam Name: Engineering Data with Microsoft Cloud Services
  • Updated: Aug 20, 2026
  • Q & A: 92 Questions and Answers
  • Software Price: $59.99
  • Testing Engine

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Overview about MICROSOFT 70-776 Exam

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

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

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MICROSOFT 70-776 Exam Reference, Microsoft Documents

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

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