Overview about MICROSOFT 70-776 Exam

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

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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

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Microsoft 070-776 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Manage and Maintain Azure SQL Data Warehouse, Azure Data Lake, Azure Data Factory, and Azure Stream Analytics	20-25%	- Design storage solutions for big data 1. Integrate cloud and on-premises solutions 2. Select storage technologies 3. Optimize storage performance 4. Migrate data - Implement authentication, authorization, and auditing 1. Secure integrated services 2. Configure firewalls 3. Implement auditing 4. Integrate with Azure Active Directory - Provision Azure services 1. Deploy Azure SQL Data Warehouse 2. Deploy Azure Data Factory 3. Deploy Azure Stream Analytics 4. Deploy Azure Data Lake - Monitor and optimize services 1. Implement elastic scaling 2. Troubleshoot performance 3. Monitor workloads 4. Manage concurrency - Manage data recovery 1. Implement geo-redundancy 2. Backup and recovery 3. Support migration scenarios
Topic 2: Design and Implement Azure SQL Data Warehouse Solutions	15-20%	- Design tables in Azure SQL Data Warehouse 1. Minimize data skew 2. Select distribution methods 3. Design columnstore indexes 4. Design table geometry - Integrate Azure SQL Data Warehouse with other services 1. Import and export data 2. Use PolyBase and data ingestion tools 3. Migrate enterprise data warehouses 4. Integrate with Azure Machine Learning - Query data in Azure SQL Data Warehouse 1. Implement query labels 2. Monitor query performance 3. Manage resource classes 4. Manage statistics
Topic 3: Design and Implement Analytics by Using Azure Data Lake	25-30%	- Extend U-SQL programmability 1. Implement user-defined functions and operators 2. Perform federated queries 3. Share code and data assets 4. Integrate Python and R - Ingest data into Azure Data Lake Store 1. Implement authentication and access control 2. Tune performance and diagnostics 3. Create and configure Data Lake Store accounts 4. Copy and secure data - Manage Azure Data Lake Analytics 1. Manage users and data sources 2. Monitor and troubleshoot jobs 3. Create and manage analytics accounts 4. Optimize jobs and review historical execution - Integrate Azure Data Lake with other services 1. Integrate with Data Catalog and Event Hubs 2. Integrate with HDInsight 3. Connect with Azure SQL Data Warehouse 4. Integrate with Azure Data Factory - Extract and transform data using U-SQL 1. Manage catalogs and structured data 2. Generate output files 3. Use U-SQL data types and expressions 4. Perform joins and analytical operations
Topic 4: Design and Implement Complex Event Processing by Using Azure Stream Analytics	15-20%	- Query real-time data 1. Use Stream Analytics query language 2. Guarantee event delivery 3. Manage time windows 4. Use built-in functions and data types - Design and implement Azure Stream Analytics 1. Support continuous learning scenarios 2. Implement scoring models 3. Integrate Azure Machine Learning 4. Configure thresholds and alerts - 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. Process streaming data sources 2. Design partitioning schemes 3. Estimate throughput and latency requirements 4. Select appropriate ingestion technologies 5. Design reference data streams
Topic 5: Design and Implement Cloud-Based Integration by Using Azure Data Factory	15-20%	- Monitor and manage Azure Data Factory 1. Identify failures 2. Use monitoring tools 3. Create alerts 4. Perform redeployments - Orchestrate data processing pipelines 1. Provision and run pipelines 2. Design schedules 3. Manage dependencies 4. Design end-to-end data flows - Implement datasets and linked services 1. Configure linked services 2. Implement availability and policies 3. Create datasets - Move, transform, and analyze data 1. Create activity types 2. Move data to and from SQL Data Warehouse 3. Extend processing with custom activities 4. Copy data between environments