Microsoft Engineering Data with Microsoft Cloud Services : 70-776 Exam Questions

Exam Code: 70-776
Exam Name: Engineering Data with Microsoft Cloud Services
Updated: Sep 13, 2026
Q&As: 92 Questions and Answers

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About Microsoft Engineering Data with Microsoft Cloud Services Exam Braindumps

Overview about MICROSOFT 70-776 Exam

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

How to book MICROSOFT 70-776 Certifications 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:

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

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