Exam 70-776 Perform Big Data Engineering on Microsoft Cloud Services (beta)

Published: July 5, 2017
Languages: English
Audiences: Data engineers
Technology: Microsoft Azure SQL Data Warehouse, Azure Data Lake Analytics, Azure Data Factory, Azure Stream Analytics
Credit toward certification: MCSA

Skills measured
This exam measures your ability to accomplish the technical tasks listed below. View video tutorials about the variety of question types on Microsoft exams.

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Design and Implement Complex Event Processing By Using Azure Stream Analytics (15-20%)
Ingest data for real-time processing
Select appropriate data ingestion technology based on specific constraints; design partitioning scheme and select mechanism for partitioning; ingest and process data from a Twitter stream; connect to stream processing entities; estimate throughput, latency needs, and job footprint; design reference data streams
Design and implement Azure Stream Analytics
Configure thresholds, use the Azure Machine Learning UDF, create alerts based on conditions, use a machine learning model for scoring, train a model for continuous learning, use common stream processing scenarios
Implement and manage the streaming pipeline
Stream data to a live dashboard, archive data as a storage artifact for batch processing, enable consistency between stream processing and batch processing logic
Query real-time data by using the Azure Stream Analytics query language
Use built-in functions, use data types, identify query language elements, control query windowing by using Time Management, guarantee event delivery

Design and Implement Analytics by Using Azure Data Lake (25-30%)
Ingest data into Azure Data Lake Store
Create an Azure Data Lake Store (ADLS) account, copy data to ADLS, secure data within ADLS by using access control, leverage end-user or service-to-service authentication appropriately, tune the performance of ADLS, access diagnostic logs
Manage Azure Data Lake Analytics
Create an Azure Data Lake Analytics (ADLA) account, manage users, manage data sources, manage, monitor, and troubleshoot jobs, access diagnostic logs, optimize jobs by using the vertex view, identify historical job information
Extract and transform data by using U-SQL
Schematize data on read at scale; generate outputter files; use the U-SQL data types, use C# and U-SQL expression language; identify major differences between T-SQL and U-SQL; perform JOINS, PIVOT, UNPIVOT, CROSS APPLY, and Windowing functions in U-SQL; share data and code through U-SQL catalog; define benefits and use of structured data in U-SQL; manage and secure the Catalog
Extend U-SQL programmability
Use user-defined functions, aggregators, and operators, scale out user-defined operators, call Python, R, and Cognitive capabilities, use U-SQL user-defined types, perform federated queries, share data and code across ADLA and ADLS
Integrate Azure Data Lake Analytics with other services
Integrate with Azure Data Factory, Azure HDInsight, Azure Data Catalog, and Azure Event Hubs, ingest data from Azure SQL Data Warehouse

Design and Implement Azure SQL Data Warehouse Solutions (15-20%)
Design tables in Azure SQL Data Warehouse
Choose the optimal type of distribution column to optimize workflows, select a table geometry, limit data skew and process skew through the appropriate selection of distributed columns, design columnstore indexes, identify when to scale compute nodes, calculate the number of distributions for a given workload
Query data in Azure SQL Data Warehouse
Implement query labels, aggregate functions, create and manage statistics in distributed tables, monitor user queries to identify performance issues, change a user resource class
Integrate Azure SQL Data Warehouse with other services
Ingest data into Azure SQL Data Warehouse by using AZCopy, Polybase, Bulk Copy Program (BCP), Azure Data Factory, SQL Server Integration Services (SSIS), Create-Table-As-Select (CTAS), and Create-External-Table-As-Select (CETAS); export data from Azure SQL Data Warehouse; provide connection information to access Azure SQL Data Warehouse from Azure Machine Learning; leverage Polybase to access a different distributed store; migrate data to Azure SQL Data Warehouse; select the appropriate ingestion method based on business needs

Design and Implement Cloud-Based Integration by using Azure Data Factory (15-20%)
Implement datasets and linked services
Implement availability for the slice, create dataset policies, configure the appropriate linked service based on the activity and the dataset
Move, transform, and analyze data by using Azure Data Factory activities
Copy data between on-premises and the cloud, create different activity types, extend the data factory by using custom processing steps, move data to and from Azure SQL Data Warehouse
Orchestrate data processing by using Azure Data Factory pipelines
Identify data dependencies and chain multiple activities, model schedules based on data dependencies, provision and run data pipelines, design a data flow
Monitor and manage Azure Data Factory
Identify failures and root causes, create alerts for specified conditions, perform a redeploy, use the Microsoft Azure Portal monitoring tool

Manage and Maintain Azure SQL Data Warehouse, Azure Data Lake, Azure Data Factory, and Azure Stream Analytics (20-25%)
Provision Azure SQL Data Warehouse, Azure Data Lake, Azure Data Factory, and Azure Stream Analytics
Provision Azure SQL Data Warehouse, Azure Data Lake, and Azure Data Factory, implement Azure Stream Analytics
Implement authentication, authorization, and auditing
Integrate services with Azure Active Directory (Azure AD), use the local security model in Azure SQL Data Warehouse, configure firewalls, implement auditing, integrate services with Azure Data Factory
Manage data recovery for Azure SQL Data Warehouse, Azure Data Lake, and Azure Data Factory, Azure Stream Analytics
Backup and recover services, plan and implement geo-redundancy for Azure Storage, migrate from an on-premises data warehouse to Azure SQL Data Warehouse
Monitor Azure SQL Data Warehouse, Azure Data Lake, and Azure Stream Analytics
Manage concurrency, manage elastic scale for Azure SQL Data Warehouse, monitor workloads by using Dynamic Management Views (DMVs) for Azure SQL Data Warehouse, troubleshoot Azure Data Lake performance by using the Vertex Execution View
Design and implement storage solutions for big data implementations
Optimize storage to meet performance needs, select appropriate storage types based on business requirements, use AZCopy, Storage Explorer and Redgate Azure Explorer to migrate data, design cloud solutions that integrate with on-premises data

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