Exam Code: 70-776
Exam Name: Engineering Data with Microsoft Cloud Services
Certification Provider: Microsoft
Corresponding Certification: Microsoft Azure SQL Data Warehous

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Updated: Sep 01, 2026

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Microsoft 70-776 Exam Overview:

Certification Vendor:	Microsoft
Exam Name:	Engineering Data with Microsoft Cloud Services
Exam Number:	70-776
Exam Format:	Build List, Multiple Choice, Scenario-Based Questions, Multiple Response, Drag and Drop, Case Study
Exam Duration:	120-150
Related Certifications:	MCSE: Data Management and Analytics MCSA: Data Engineering with Azure
Passing Score:	700/1000
Available Languages:	English
Certificate Validity Period:	Certification retired; exam retired June 30, 2019
Real Exam Qty:	40-60
Exam Price:	USD 165
Sample Questions:	Microsoft 70-776 Sample Questions
Exam Way:	Pearson VUE testing centers worldwide; retired June 30, 2019.
Pre Condition:	No mandatory prerequisite exam. Intended for data engineers, data architects, data scientists, and data developers. Common prerequisites included Azure data services, relational databases, Windows administration, and data analytics experience.
Official Syllabus URL:	https://learn.microsoft.com/en-us/credentials/

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

itPass4sure 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.

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

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

Microsoft 70-776 Exam Syllabus Topics:

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

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