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70-776 - Performing Big Data Engineering with Microsoft Cloud Services - Dump Information
Vendor | : | Microsoft |
Exam Code | : | 70-776 |
Exam Name | : | Performing Big Data Engineering with Microsoft Cloud Services |
Questions and Answers | : | 69 Q & A |
Updated On | : | January 1, 2018 |
PDF Download Mirror | : | 70-776 Brain Dump |
Get Full Version | : | Pass4sure 70-776 Full Version |
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70-776 Microsoft Performing Big Data Engineering with Microsoft Cloud Services http://killexams.com/pass4sure/exam-detail/70-776 QUESTION: 63 Start of Repeated Scenario: You are migrating an existing on premises data warehouse named LocalDW to Microsoft Azure. You will use an Azure SQL data warehouse named AzureDW for data storaqe and an Azure Data Factory named AzureDF for extract, transformation, and load (ETL) functions. For each table in LocalDW, you create a table in AzureDW. On the on premises network, you have a Data Management Gateway. Some source data is stored in Azure Blob storage. Some source data is stored on an onpremises Microsoft SQL Server instance. The instance has a table named Table1. After data is processed by using AzureDF, the data must be archived and accessible forever. The archived data must meet a Service Level Agreement (SLA) for availability of 99 percent. If an Azure region fails the archived data must be available for reading always. The storage solution for the archived data must minimize costs. End of Repeated Scenario You need to configure Azure Data Factory to connect to the on premises SQL Server instance. What should you do first? A. D eploy an Azure virtual network gateway. B. Create a dataset in Azure Data Factory. C. From Azure Data Factory, define a data gateway. D. Deploy an Azure local network gateway. Answer: A QUESTION: 64 HOTSPOT You have a Microsoft Azure Data lake Analytics service. You have a tab-delimited die named UserActivity.tsv that contains logs of user sessions. The file does not have a header row. You need to create a table and to load the logs to the table. The solution must distribute the data by a column named Sessionld. How should you complete the U-SQL statement? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point. An swer: Exhibit QUESTION: 65 You have a Microsoft Azure SQL data warehouse that contains information about community events. An Azure Data Factory job writes an updated CSV file in Azure Blob storage to Community/(date)/event.csv daily. You plan to consume a Twitter feed by using Azure Stream Analytics and to correlate the feed to the community events. You plan to use Stream Analytics to retrieve the latest community events data and to correlate the data to the Twitter teed data. You need to ensure that when updates to the community events data is written to the CSV files, the Stream Analytics job can access the latest community events data. What should you configure? A. an output that uses a blob storage sink and has a path pattern of Community/(date) B. an output that uses an event hub sink and the CSV event serialization format C. an input that uses a reference data source and has a path pattern of Community/(date)/event.csv D. an input that uses a reference data source and has a path pattern of Community/(date)/event.csv Answer: D QUESTION: 66 You are developing an application that uses Microsoft Azure Stream Analytics. You have data structures that are defined dynamically. You want to enable consistency between the logical methods used by stream processing and batch processing. You need to ensure that the data can be integrated by using consistent data points. What should you use to process the data? A. a vectorized Microsoft SQL Server Database Engine B. directed acyclic graph (DAG) C. Apache Spark queries that use updateStateByKey operators D. Apache Spark queries that use mapWithState operators Answer: D QUESTION: 67 Start of Repeated Scenario: You are migrating an existing on premises data warehouse named LocalDW to Microsoft Azure. You will use an Azure SQL data warehouse named AzureDW for data storaqe and an Azure Data Factory named AzureDF for extract, transformation, and load (ETL) functions. For each table in LocalDW, you create a table in AzureDW. On the on premises network, you have a Data Management Gateway. Some source data is stored in Azure Blob storage. Some source data is stored on an on premises Microsoft SQL Server instance. The instance has a table named Table1. After data is processed by using AzureDF, the data must be archived and accessible forever. The archived data must meet a Service Level Agreement (SLA) for availability of 99 percent. If an Azure region fails the archived data must be available for reading always. The storage solution for the archived data must minimize costs. End of Repeated Scenario You need to configure an activity to move data from blob storage to AzureDW. What should you create? A. a pipeline B. a linked service C. an automation runbook D. a dataset Answer: A QUESTION: 68 You have a Microsoft Azure Data Factory that recently ran several activities in parallel. You receive alerts indicating that there are insufficient resources. From the Activity Windows list in the Monitoring and Management app, you discover the statuses described in the following table. Which activity cannot complete because of insufficient resources? A. Activity 2 B. Activity 4 C. Activity 5 D. Activity 7 Answer: C QUESTION: 69 You are monitoring user queries to a Microsoft Azure SQL data warehouse that has six compute nodes. You discover that compute node utilization is uneven. The rows_processed column from sys.dm_pdw_dms_workers shows a significant variation in the number of rows being moved among the distributions for the same table for the same query. You need to ensure that the load is distributed evenly across the compute nodes. Solution: You change the table to use a column that is not skewed for hash distribution. Does this meet the goal? A. Yes B. No Answer: A For More exams visit http://killexams.com Kill your exam at First Attempt....Guaranteed!Microsoft 70-776 Exam (Performing Big Data Engineering with Microsoft Cloud Services) Detailed Information
70-776 - Performing Big Data Engineering with Microsoft Cloud Services
70-776 Test Objectives
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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
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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
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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
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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
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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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