Exam Code: 70-776
Exam Name: Engineering Data with Microsoft Cloud Services
Certification Provider: Microsoft
Corresponding Certification: Microsoft Azure SQL Data Warehous
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  • Select Date and Center of examination and confirm with payment value of 165$
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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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Reference: https://www.microsoft.com/en-us/learning/exam-70-776.aspx

Microsoft 70-776 Exam Syllabus Topics:

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

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