070-776 exam dumps

Microsoft 070-776 Value Package

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  • Exam Code: 070-776
  • Exam Name: Engineering Data with Microsoft Cloud Services
  • No. of Questions: 92 Questions and Answers
  • Updated: Sep 12, 2026

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Microsoft 070-776 exam consists of 40-60 questions, which must be completed within 120 minutes. 070-776 exam fee is $165 USD, and candidates can take it online or at a testing center. The passing score for the exam is 700 out of 1000, so candidates must ensure that they have a solid understanding of the exam objectives before attempting it.

Passing the Microsoft 070-776 certification exam demonstrates that a candidate has the skills and knowledge required to engineer data solutions using Microsoft cloud services. Engineering Data with Microsoft Cloud Services certification is suitable for data engineers, data analysts, database administrators, and other professionals who work with data. It is also an excellent credential for those who wish to advance their careers in the cloud computing industry and become experts in data engineering with Microsoft Azure services.

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

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Microsoft 070-776 exam, also known as Engineering Data with Microsoft Cloud Services, is designed for IT professionals who are responsible for managing data using Microsoft Cloud Services. 070-776 exam covers various topics, including the design and implementation of data storage solutions, the deployment and management of data processing services, and the integration of data with other Microsoft Cloud Services. 070-776 exam is intended for individuals who have a solid understanding of Microsoft Azure services and are familiar with data processing technologies such as Azure Data Factory and Azure Databricks.

Microsoft 070-776 Exam Syllabus Topics:

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

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