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Microsoft 070-776 valid - in .pdf

Exam Code: 070-776
Exam Name: Engineering Data with Microsoft Cloud Services
Q & A: 92 Questions and Answers
PDF Price: $59.99
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Exam Code: 070-776
Exam Name: Engineering Data with Microsoft Cloud Services
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Exam Code: 070-776
Exam Name: Engineering Data with Microsoft Cloud Services
Q & A: 92 Questions and Answers
Software Price: $59.99
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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:

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

Perform Big Data Engineering on Microsoft Cloud Services (beta) 70-776 Dumps will include below mentioned topics with Exam focused percentage

Design and Implement Analytics by Using Azure Data Lake: 25-30%
Design and Implement Azure SQL Data Warehouse Solutions: 15-20%
Design and Implement Cloud-Based Integration by using Azure Data Factory: 15-20%
Design and Implement Complex Event Processing By Using Azure Stream Analytics: 15-20%
Manage and Maintain Azure SQL Data Warehouse, Azure Data Lake, Azure Data Factory, and Azure Stream Analytics: 20-25%

Perform Big Data Engineering on Microsoft Cloud Services (beta) 70-776 Dumps Provided Study Notes

ValidBraindumps expert team recommend you to prepare some notes on these topics along with it don't forget to practice Perform Big Data Engineering on Microsoft Cloud Services (beta) 70-776 Dumps which been written by our expert team.

Both these will help you a lot to clear this exam with good marks.

Azcopy
Azure Backup Services
Azure Event Hubs
Azure Stream Analytics
SQL Server Integration Services
Azure Data Factory
T-SQL/U-SQL
Azure Data Factory Pipelines
Azure Data Lake
Azure Data Warehouse
Azure Storage
Azure Storage Explorer
Azure Machine Learning
Bulk Copy Program

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

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Microsoft 070-776 Exam Syllabus Topics:

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

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