Microsoft Engineering Data with Microsoft Cloud Services : 070-776

Exam Code: 070-776
Exam Name: Engineering Data with Microsoft Cloud Services
Updated: Oct 10, 2026
Q & A: 92 Questions and Answers

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About Microsoft 070-776 Exam

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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
Cloud based integration using Azure Data Factory.
Backup and Recovery Service Plan for Azure Storage
Monitor Azure SQL Data Warehouse, Azure Data Lake, and Azure Stream Analytics
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
Azure Data Factory Pipelines.

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

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

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

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Microsoft 070-776 Exam Syllabus Topics:

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

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