Engineering Data with Microsoft Cloud Services: 070-776 Exam

"Engineering Data with Microsoft Cloud Services", also known as 070-776 exam, is a Microsoft Certification. With the complete collection of questions and answers, iPassleader has assembled to take you through 92 Q&As to your 070-776 Exam preparation. In the 070-776 exam resources, you will cover every field and category in Microsoft Azure SQL Data Warehous Certification helping to ready you for your successful Microsoft Certification.

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  • Updated on: Oct 22, 2026
  • No. of Questions: 92 Questions & Answers

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  • No. of Questions: 92 Questions & Answers

Knowing your weaknesses is the fastest route to fixing them. The online test engine for the Microsoft Engineering Data with Microsoft Cloud Services exam points out your mistakes and reminds you to review them daily, and iPassleader pairs it with 92 verified practice questions.

Microsoft 070-776 Exam Overview:

Certification Vendor:Microsoft
Exam Name:Engineering Data with Microsoft Cloud Services
Exam Number:70-776
Real Exam Qty:40-60
Certificate Validity Period:Certification retired; exam retired June 30, 2019
Exam Price:USD 165
Passing Score:700/1000
Exam Duration:120-150
Exam Format:Multiple Choice, Scenario-Based Questions, Multiple Response, Drag and Drop, Case Study, Build List
Available Languages:English
Related Certifications:MCSA: Data Engineering with Azure
MCSE: Data Management and Analytics
Sample Questions: DOWNLOAD DEMO
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/

Microsoft 070-776 Exam Syllabus Topics:

SectionWeightObjectives
Design and Implement Analytics by Using Azure Data Lake25-30%- Extract and transform data using U-SQL
  • 1. Perform joins and analytical operations
  • 2. Manage catalogs and structured data
  • 3. Generate output files
  • 4. Use U-SQL data types and expressions
- Ingest data into Azure Data Lake Store
  • 1. Implement authentication and access control
  • 2. Copy and secure data
  • 3. Create and configure Data Lake Store accounts
  • 4. Tune performance and diagnostics
- Manage Azure Data Lake Analytics
  • 1. Manage users and data sources
  • 2. Monitor and troubleshoot jobs
  • 3. Create and manage analytics accounts
  • 4. Optimize jobs and review historical execution
- 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 Data Catalog and Event Hubs
  • 2. Integrate with HDInsight
  • 3. Integrate with Azure Data Factory
  • 4. Connect with Azure SQL Data Warehouse
Manage and Maintain Azure SQL Data Warehouse, Azure Data Lake, Azure Data Factory, and Azure Stream Analytics20-25%- Design storage solutions for big data
  • 1. Select storage technologies
  • 2. Integrate cloud and on-premises solutions
  • 3. Optimize storage performance
  • 4. Migrate data
- Monitor and optimize services
  • 1. Implement elastic scaling
  • 2. Monitor workloads
  • 3. Manage concurrency
  • 4. Troubleshoot performance
- Manage data recovery
  • 1. Support migration scenarios
  • 2. Implement geo-redundancy
  • 3. Backup and recovery
- 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. Configure firewalls
  • 2. Secure integrated services
  • 3. Integrate with Azure Active Directory
  • 4. Implement auditing
Design and Implement Complex Event Processing by Using Azure Stream Analytics15-20%- Design and implement Azure Stream Analytics
  • 1. Configure thresholds and alerts
  • 2. Support continuous learning scenarios
  • 3. Implement scoring models
  • 4. Integrate Azure Machine Learning
- Ingest data for real-time processing
  • 1. Design partitioning schemes
  • 2. Design reference data streams
  • 3. Process streaming data sources
  • 4. Estimate throughput and latency requirements
  • 5. Select appropriate ingestion technologies
- Implement and manage streaming pipelines
  • 1. Coordinate stream and batch processing
  • 2. Archive streaming data
  • 3. Stream data to dashboards
- Query real-time data
  • 1. Use built-in functions and data types
  • 2. Guarantee event delivery
  • 3. Manage time windows
  • 4. Use Stream Analytics query language
Design and Implement Azure SQL Data Warehouse Solutions15-20%- Integrate Azure SQL Data Warehouse with other services
  • 1. Migrate enterprise data warehouses
  • 2. Use PolyBase and data ingestion tools
  • 3. Import and export data
  • 4. Integrate with Azure Machine Learning
- Design tables in Azure SQL Data Warehouse
  • 1. Select distribution methods
  • 2. Design table geometry
  • 3. Design columnstore indexes
  • 4. Minimize data skew
- Query data in Azure SQL Data Warehouse
  • 1. Manage resource classes
  • 2. Manage statistics
  • 3. Implement query labels
  • 4. Monitor query performance
Design and Implement Cloud-Based Integration by Using Azure Data Factory15-20%- Implement datasets and linked services
  • 1. Configure linked services
  • 2. Create datasets
  • 3. Implement availability and policies
- Orchestrate data processing pipelines
  • 1. Design end-to-end data flows
  • 2. Manage dependencies
  • 3. Design schedules
  • 4. Provision and run pipelines
- Move, transform, and analyze data
  • 1. Copy data between environments
  • 2. Move data to and from SQL Data Warehouse
  • 3. Create activity types
  • 4. Extend processing with custom activities
- Monitor and manage Azure Data Factory
  • 1. Identify failures
  • 2. Perform redeployments
  • 3. Create alerts
  • 4. Use monitoring tools

What to Know Before Buying 070-776 Material

After each practice session, the online test engine records which questions you answered incorrectly and reminds you to revisit them in your next round. Reviewing your own mistakes daily shows you clearly where your weaknesses and strengths lie, so every session builds on the last instead of starting from scratch.

The Microsoft Engineering Data with Microsoft Cloud Services exam contains 40-60 questions, and you will have 120-150 minutes to complete them. Practicing the full volume under a timer is the closest thing to rehearsing the real exam.

Yes. The online test engine lets you set a time limit for each practice session, so you can train yourself to allocate minutes per question the way you will need to in the real 070-776 exam. Working under a clock at home makes time pressure on exam day feel routine rather than alarming.

The Microsoft Engineering Data with Microsoft Cloud Services exam requires a score of 700/1000 to pass, and the official registration fee is USD 165. Since the exam fee is a real investment, thorough preparation is the most practical way to protect it.

The Microsoft Engineering Data with Microsoft Cloud Services exam is scored on a scale where 700/1000 is required to pass, and the official exam fee is USD 165.

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.

You can confirm the current eligibility requirements on the official 070-776 exam page before you register.

The Microsoft Engineering Data with Microsoft Cloud Services practice questions are organized around the official exam objectives. Key topics include:

  • Manage and Maintain Azure SQL Data Warehouse, Azure Data Lake, Azure Data Factory, and Azure Stream Analytics (20-25%)
  • Design and Implement Complex Event Processing by Using Azure Stream Analytics (15-20%)
  • Design and Implement Analytics by Using Azure Data Lake (25-30%)

Daily practice mapped to these topics keeps your preparation balanced across the whole syllabus.

This package includes 92 practice questions for the Microsoft Engineering Data with Microsoft Cloud Services exam. Every answer is verified by an experienced team of experts, so you can trust what you study.

Microsoft Engineering Data with Microsoft Cloud Services Sample Questions:

You have an on-premises Microsoft SQL Server instance named Instance1 that contains a database named DB1.
You have a Data Management Gateway named Gateway1.
You plan to create a linked service in Azure Data Factory for DB1.
You need to connect to DB1 by using standard SQL Server Authentication. You must use a username of User1 and a password of P@$$w0rd89.
How should you complete the JSON code? TO answer, drag the appropriate values to the correct targets. Each value may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.

Reveal Solution  Discussion  0

Correct Answer:


Explanation

References:
https://github.com/uglide/azure-content/blob/master/articles/data-factory/data-factory-move-data-between-onpre

You have a Microsoft Azure Data Lake Analytics service.
You have a file named Employee.tsv that contains data on employees. Employee.tsv contains seven columns named EmpId, Start, FirstName, LastName, Age, Department, and Title.
You need to create a Data Lake Analytics jobs to transform Employee.tsv, define a schema for the data, and output the data to a CSV file. The outputted data must contain only employees who are in the sales department. The Age column must allow NULL.
How should you complete the U-SQL code segment? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Reveal Solution  Discussion  0

Correct Answer:


Explanation

References:
https://docs.microsoft.com/en-us/azure/data-lake-analytics/data-lake-analytics-u-sql-get-started

You need to create a linked service in Microsoft Azure Data Factory. The linked service must use an Azure Database for MySQL table named Customers.
How should you complete the JSON snippet? To answer, drag the appropriate values to the correct targets.
Each value may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.

Reveal Solution  Discussion  0

Correct Answer:


Explanation

You have a Microsoft Azure Stream Analytics job.
You are debugging event information manually.
You need to view the event data that is being collected.
Which monitoring data should you view for the Stream Analytics job?

  • A. query
  • B. scale
  • C. inputs
  • D. outputs
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

You are using a Microsoft Azure Stream Analytics query language.
You are outputting data from an input click stream.
You need to ensure that when you consecutively receive two rows from the same IP address within one minute, only the first row is outputted.
Which functions should you use in the WHERE statement?

  • A. Last and HoppingWindow
  • B. LAG and Duration
  • C. LAG and HoppingWindow
  • D. Last and SlidingWindow
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

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