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The Microsoft 070-776 exam consists of 40 to 60 questions that are randomly selected from Microsoft's exam question pool. Prospective candidates should have experience with handling data integration, modeling techniques, designing data storage, and data flow processes. They should also have knowledge of the Microsoft cloud ecosystem and tools such as Azure Data Lake, Power BI, Azure SQL Data Warehouse, and Azure Stream Analytics. 070-776 exam is open to anyone that is interested in pursuing a career in cloud computing, data integration, and data science.

Microsoft 070-776: Engineering Data with Microsoft Cloud Services exam is an excellent opportunity for IT professionals to showcase their expertise in designing and implementing data solutions using Microsoft Azure. 070-776 exam covers a wide range of topics, including data engineering, big data processing, data storage, and data analytics. IT professionals who pass 070-776 exam and earn the Microsoft Certified: Azure Data Engineer Associate certification will have a distinct advantage in the job market and can expect to receive better job opportunities and higher salaries.

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

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Microsoft 070-776 certification exam covers a broad range of topics, including data storage and management, data analytics, and data processing using various Microsoft cloud services such as Azure Data Lake Storage, Azure HDInsight, and Azure Data Factory. 070-776 exam also evaluates the candidate's ability to design and implement secure and scalable cloud solutions for engineering data.

For more info visit:

MICROSOFT 70-776 Exam Reference, Microsoft Documents

Microsoft 070-776 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Manage and Maintain Azure SQL Data Warehouse, Azure Data Lake, Azure Data Factory, and Azure Stream Analytics20-25%- Provision Azure services
  • 1. Deploy Azure Stream Analytics
  • 2. Deploy Azure Data Factory
  • 3. Deploy Azure Data Lake
  • 4. Deploy Azure SQL Data Warehouse
- Manage data recovery
  • 1. Backup and recovery
  • 2. Support migration scenarios
  • 3. Implement geo-redundancy
- Design storage solutions for big data
  • 1. Integrate cloud and on-premises solutions
  • 2. Migrate data
  • 3. Optimize storage performance
  • 4. Select storage technologies
- 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. Troubleshoot performance
  • 3. Monitor workloads
  • 4. Implement elastic scaling
Topic 2: 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. Implement authentication and access control
  • 2. Create and configure Data Lake Store accounts
  • 3. Copy and secure data
  • 4. Tune performance and diagnostics
- Manage Azure Data Lake Analytics
  • 1. Manage users and data sources
  • 2. Optimize jobs and review historical execution
  • 3. Create and manage analytics accounts
  • 4. Monitor and troubleshoot jobs
- Extract and transform data using U-SQL
  • 1. Perform joins and analytical operations
  • 2. Use U-SQL data types and expressions
  • 3. Generate output files
  • 4. Manage catalogs and structured data
- Integrate Azure Data Lake with other services
  • 1. Connect with Azure SQL Data Warehouse
  • 2. Integrate with HDInsight
  • 3. Integrate with Azure Data Factory
  • 4. Integrate with Data Catalog and Event Hubs
Topic 3: Design and Implement Cloud-Based Integration by Using Azure Data Factory15-20%- Orchestrate data processing pipelines
  • 1. Design schedules
  • 2. Provision and run pipelines
  • 3. Design end-to-end data flows
  • 4. Manage dependencies
- Implement datasets and linked services
  • 1. Implement availability and policies
  • 2. Configure linked services
  • 3. Create datasets
- 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. Perform redeployments
  • 2. Create alerts
  • 3. Identify failures
  • 4. Use monitoring tools
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. Manage statistics
  • 4. Monitor query performance
- Design tables in Azure SQL Data Warehouse
  • 1. Design columnstore indexes
  • 2. Design table geometry
  • 3. Minimize data skew
  • 4. Select distribution methods
- Integrate Azure SQL Data Warehouse with other services
  • 1. Import and export data
  • 2. Integrate with Azure Machine Learning
  • 3. Use PolyBase and data ingestion tools
  • 4. Migrate enterprise data warehouses
Topic 5: Design and Implement Complex Event Processing by Using Azure Stream Analytics15-20%- Query real-time data
  • 1. Guarantee event delivery
  • 2. Use built-in functions and data types
  • 3. Manage time windows
  • 4. Use Stream Analytics query language
- Ingest data for real-time processing
  • 1. Process streaming data sources
  • 2. Estimate throughput and latency requirements
  • 3. Design partitioning schemes
  • 4. Design reference data streams
  • 5. Select appropriate ingestion technologies
- Implement and manage streaming pipelines
  • 1. Coordinate stream and batch processing
  • 2. Archive streaming data
  • 3. Stream data to dashboards
- Design and implement Azure Stream Analytics
  • 1. Support continuous learning scenarios
  • 2. Integrate Azure Machine Learning
  • 3. Implement scoring models
  • 4. Configure thresholds and alerts

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