Microsoft 70-776 Q&A - in .pdf

  • Exam Code: 70-776
  • Exam Name: Engineering Data with Microsoft Cloud Services
  • Updated: Jul 18, 2026
  • Q & A: 92 Questions and Answers
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Microsoft 70-776 Q&A - Testing Engine

  • Exam Code: 70-776
  • Exam Name: Engineering Data with Microsoft Cloud Services
  • Updated: Jul 18, 2026
  • Q & A: 92 Questions and Answers
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Microsoft 070-776 certification exam is suitable for professionals who work with data and want to gain expertise in engineering data with Microsoft Cloud Services. 70-776 exam covers various topics, including Azure data storage, Azure data processing, and Azure data visualization. Candidates will be evaluated on their ability to design and implement data storage solutions using Azure Blob storage, Azure Table storage, and Azure File storage. They will also be tested on their proficiency in designing and implementing data processing solutions using Azure HDInsight, Azure Data Factory, and Azure Stream Analytics. Additionally, candidates will be evaluated on their ability to design and implement data visualization solutions using Azure Power BI.

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Candidates taking the 070-776 exam should have a good understanding of data engineering concepts, as well as experience with programming languages such as Python, C#, and Java. They should also possess knowledge of SQL and relational databases. Prior experience with Microsoft Cloud Services is not required, but it is recommended.

Microsoft 070-776 exam consists of 40-60 questions, which must be completed within 120 minutes. 70-776 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.

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

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Microsoft is a technology giant and has developed some of the most sought-after software products in the world. The company offers a variety of certification programs that allow individuals to demonstrate their proficiency in different Microsoft products and services. One such certification program is the Microsoft 070-776 exam. Engineering Data with Microsoft Cloud Services certification exam is specifically designed to test an individual's proficiency in engineering data with Microsoft cloud services.

Microsoft 70-776 Exam Syllabus Topics:

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

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