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Perform Big Data Engineering on Microsoft Cloud Services 70-776 Exam

Perform Big Data Engineering on Microsoft Cloud Services 70-776 Exam is intended mainly to design analytical solutions and build operational solutions on Azure. Candidates for this exam have relevant work experience in data engineering issues with Azure SQL Data Warehouse, Azure Data Lake, Azure Data Factory, and Azure Stream Analytics. Candidates are familiar with the features and capabilities of batch data processing, real-time processing, and operationalization technologies.

Candidates for this exam can apply the Microsoft cloud ecosystem to designs and can implement big data analytics solutions to meet their project's needs. In an effort to expand out the breadth of certifications and exams offered around Microsoft Azure and the cloud, Microsoft has added the Perform Big Data Engineering on Microsoft Cloud Services 70-776 Exam. This exam only available for the candidates till 30th June 2019 after that this may get replace with some other exam so you can plan get it completed before it get expire.

We think our Perform Big Data Engineering on Microsoft Cloud Services (beta) 70-776 Exam Practice Test Paper and Dumps will provide you 100% confidence to make you appear for MICROSOFT 70-776 Exam.

This is the list of the contents in our Perform Big Data Engineering on Microsoft Cloud Services 70-776 Practice Test:

migrate from an on-premises data warehouse to Azure SQL Data Warehouse
Monitor Azure SQL Data Warehouse, Azure Data Lake, and Azure Stream Analytics
Cloud based integration using Azure Data Factory.
Azure Data Factory Pipelines.
Design Storage Solutions
Backup and Recovery Service Plan for Azure Storage
Provision Azure SQL Data Warehouse, Azure Data Lake, Azure Data Factory, and Azure Stream Analytics

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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

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Microsoft 70-776 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%	- Manage data recovery 1. Backup and recovery 2. Implement geo-redundancy 3. Support migration scenarios - Design storage solutions for big data 1. Optimize storage performance 2. Migrate data 3. Select storage technologies 4. Integrate cloud and on-premises solutions - Monitor and optimize services 1. Manage concurrency 2. Monitor workloads 3. Implement elastic scaling 4. Troubleshoot performance - Provision Azure services 1. Deploy Azure Data Factory 2. Deploy Azure Stream Analytics 3. Deploy Azure Data Lake 4. Deploy Azure SQL Data Warehouse - Implement authentication, authorization, and auditing 1. Secure integrated services 2. Implement auditing 3. Configure firewalls 4. Integrate with Azure Active Directory
Topic 2: Design and Implement Analytics by Using Azure Data Lake	25-30%	- Extend U-SQL programmability 1. Perform federated queries 2. Integrate Python and R 3. Share code and data assets 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. Optimize jobs and review historical execution 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. Integrate with Data Catalog and Event Hubs 2. Integrate with HDInsight 3. Connect with Azure SQL Data Warehouse 4. Integrate with Azure Data Factory - Ingest data into Azure Data Lake Store 1. Create and configure Data Lake Store accounts 2. Implement authentication and access control 3. Copy and secure data 4. Tune performance and diagnostics
Topic 3: Design and Implement Azure SQL Data Warehouse Solutions	15-20%	- Integrate Azure SQL Data Warehouse with other services 1. Migrate enterprise data warehouses 2. Integrate with Azure Machine Learning 3. Import and export data 4. Use PolyBase and data ingestion tools - Query data in Azure SQL Data Warehouse 1. Manage resource classes 2. Implement query labels 3. Monitor query performance 4. Manage statistics - Design tables in Azure SQL Data Warehouse 1. Design table geometry 2. Design columnstore indexes 3. Select distribution methods 4. Minimize data skew
Topic 4: Design and Implement Cloud-Based Integration by Using Azure Data Factory	15-20%	- Orchestrate data processing pipelines 1. Provision and run pipelines 2. Design end-to-end data flows 3. Design schedules 4. Manage dependencies - Implement datasets and linked services 1. Create datasets 2. Configure linked services 3. Implement availability and policies - Monitor and manage Azure Data Factory 1. Identify failures 2. Create alerts 3. Perform redeployments 4. Use monitoring tools - 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
Topic 5: Design and Implement Complex Event Processing by Using Azure Stream Analytics	15-20%	- Implement and manage streaming pipelines 1. Stream data to dashboards 2. Coordinate stream and batch processing 3. Archive streaming data - 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. Estimate throughput and latency requirements 3. Process streaming data sources 4. Select appropriate ingestion technologies 5. Design reference data streams - Query real-time data 1. Guarantee event delivery 2. Use built-in functions and data types 3. Use Stream Analytics query language 4. Manage time windows