(December-2019-New)Braindump2go DP-200 PDF Dumps Free Share

December/2019 New Braindump2go DP-200 Exam Dumnps with PDF and VCE Free Updated Today! Following are some DP-200 Real Exam Questions,

New Question
Case Study 3 – Litware
Overview
General Overview
Litware, Inc, is an international car racing and manufacturing company that has 1,000 employees. Most employees are located in Europe. The company supports racing teams that complete in a worldwide racing series.
Physical Locations
Litware has two main locations: a main office in London, England, and a manufacturing plant in Berlin, Germany.
During each race weekend, 100 engineers set up a remote portable office by using a VPN to connect the datacentre in the London office. The portable office is set up and torn down in approximately 20 different countries each year.
Existing environment
Race Central
During race weekends, Litware uses a primary application named Race Central. Each car has several sensors that send real-time telemetry data to the London datacentre. The data is used for real-time tracking of the cars.
Race Central also sends batch updates to an application named Mechanical Workflow by using Microsoft SQL Server Integration Services (SSIS).
The telemetry data is sent to a MongoDB database. A custom application then moves the data to databases in SQL Server 2017. The telemetry data in MongoDB has more than 500 attributes. The application changes the attribute names when the data is moved to SQL Server 2017.
The database structure contains both OLAP and OLTP databases.
Mechanical Workflow
Mechanical Workflow is used to track changes and improvements made to the cars during their lifetime.
Currently, Mechanical Workflow runs on SQL Server 2017 as an OLAP system.
Mechanical Workflow has a named Table1 that is 1 TB. Large aggregations are performed on a single column of Table 1.
Requirements
Planned Changes
Litware is the process of rearchitecting its data estate to be hosted in Azure. The company plans to decommission the London datacentre and move all its applications to an Azure datacentre.
Technical Requirements
Litware identifies the following technical requirements:
Data collection for Race Central must be moved to Azure Cosmos DB and Azure SQL Database. The data must be written to the Azure datacentre closest to each race and must converge in the least amount of time.
The query performance of Race Central must be stable, and the administrative time it takes to perform optimizations must be minimized.
The datacentre for Mechanical Workflow must be moved to Azure SQL data Warehouse.
Transparent data encryption (IDE) must be enabled on all data stores, whenever possible.
An Azure Data Factory pipeline must be used to move data from Cosmos DB to SQL Database for Race Central. If the data load takes longer than 20 minutes, configuration changes must be made to Data Factory.
The telemetry data must migrate toward a solution that is native to Azure.
The telemetry data must be monitored for performance issues. You must adjust the Cosmos DB Request Units per second (RU/s) to maintain a performance SLA while minimizing the cost of the Ru/s.
Data Masking Requirements
During rare weekends, visitors will be able to enter the remote portable offices. Litware is concerned that some proprietary information might be exposed. The company identifies the following data masking requirements for the Race Central data that will be stored in SQL Database:
Only show the last four digits of the values in a column named SuspensionSprings.
Only Show a zero value for the values in a column named ShockOilWeight.
You are monitoring the Data Factory pipeline that runs from Cosmos DB to SQL Database for Race Central.
You discover that the job takes 45 minutes to run.
What should you do to improve the performance of the job?

A. Decrease parallelism for the copy activities.
B. Increase that data integration units.
C. Configure the copy activities to use staged copy.
D. Configure the copy activities to perform compression.

Answer: B
Explanation:
Performance tuning tips and optimization features. In some cases, when you run a copy activity in Azure Data Factory, you see a “Performance tuning tips” message on top of the copy activity monitoring, as shown in the following example. The message tells you the bottleneck that was identified for the given copy run. It also guides you on what to change to boost copy throughput. The performance tuning tips currently provide suggestions like:
Use PolyBase when you copy data into Azure SQL Data Warehouse.
Increase Azure Cosmos DB Request Units or Azure SQL Database DTUs (Database Throughput
Units) when the resource on the data store side is the bottleneck.
Remove the unnecessary staged copy.

New Question
Case Study 3 – Litware
Overview
General Overview
Litware, Inc, is an international car racing and manufacturing company that has 1,000 employees. Most employees are located in Europe. The company supports racing teams that complete in a worldwide racing series.
Physical Locations
Litware has two main locations: a main office in London, England, and a manufacturing plant in Berlin, Germany.
During each race weekend, 100 engineers set up a remote portable office by using a VPN to connect the datacentre in the London office. The portable office is set up and torn down in approximately 20 different countries each year.
Existing environment
Race Central
During race weekends, Litware uses a primary application named Race Central. Each car has several sensors that send real-time telemetry data to the London datacentre. The data is used for real-time tracking of the cars.
Race Central also sends batch updates to an application named Mechanical Workflow by using Microsoft SQL Server Integration Services (SSIS).
The telemetry data is sent to a MongoDB database. A custom application then moves the data to databases in SQL Server 2017. The telemetry data in MongoDB has more than 500 attributes. The application changes the attribute names when the data is moved to SQL Server 2017.
The database structure contains both OLAP and OLTP databases.
Mechanical Workflow
Mechanical Workflow is used to track changes and improvements made to the cars during their lifetime.
Currently, Mechanical Workflow runs on SQL Server 2017 as an OLAP system.
Mechanical Workflow has a named Table1 that is 1 TB. Large aggregations are performed on a single column of Table 1.
Requirements
Planned Changes
Litware is the process of rearchitecting its data estate to be hosted in Azure. The company plans to decommission the London datacentre and move all its applications to an Azure datacentre.
Technical Requirements
Litware identifies the following technical requirements:
Data collection for Race Central must be moved to Azure Cosmos DB and Azure SQL Database. The data must be written to the Azure datacentre closest to each race and must converge in the least amount of time.
The query performance of Race Central must be stable, and the administrative time it takes to perform optimizations must be minimized.
The datacentre for Mechanical Workflow must be moved to Azure SQL data Warehouse.
Transparent data encryption (IDE) must be enabled on all data stores, whenever possible.
An Azure Data Factory pipeline must be used to move data from Cosmos DB to SQL Database for Race Central. If the data load takes longer than 20 minutes, configuration changes must be made to Data Factory.
The telemetry data must migrate toward a solution that is native to Azure.
The telemetry data must be monitored for performance issues. You must adjust the Cosmos DB Request Units per second (RU/s) to maintain a performance SLA while minimizing the cost of the Ru/s.
Data Masking Requirements
During rare weekends, visitors will be able to enter the remote portable offices. Litware is concerned that some proprietary information might be exposed. The company identifies the following data masking requirements for the Race Central data that will be stored in SQL Database:
Only show the last four digits of the values in a column named SuspensionSprings.
Only Show a zero value for the values in a column named ShockOilWeight.
What should you implement to optimize SQL Database for Race Central to meet the technical requirements?

A. the sp_update stored procedure
B. automatic tuning
C. Query Store
D. the dbcc checkdb command

Answer: A
Explanation:
Scenario: The query performance of Race Central must be stable, and the administrative time it takes to perform optimizations must be minimized.
updates query optimization statistics on a table or indexed view. By default, the query sp_update
optimizer already updates statistics as necessary to improve the query plan; in some cases you can improve query performance by using UPDATE STATISTICS or the stored procedure sp_updatestats to update statistics more frequently than the default updates.
Incorrect Answers:
D: dbcc checkdchecks the logical and physical integrity of all the objects in the specified database

New Question
Case Study 3 – Litware
Overview
General Overview
Litware, Inc, is an international car racing and manufacturing company that has 1,000 employees. Most employees are located in Europe. The company supports racing teams that complete in a worldwide racing series.
Physical Locations
Litware has two main locations: a main office in London, England, and a manufacturing plant in Berlin, Germany.
During each race weekend, 100 engineers set up a remote portable office by using a VPN to connect the datacentre in the London office. The portable office is set up and torn down in approximately 20 different countries each year.
Existing environment
Race Central
During race weekends, Litware uses a primary application named Race Central. Each car has several sensors that send real-time telemetry data to the London datacentre. The data is used for real-time tracking of the cars.
Race Central also sends batch updates to an application named Mechanical Workflow by using Microsoft SQL Server Integration Services (SSIS).
The telemetry data is sent to a MongoDB database. A custom application then moves the data to databases in SQL Server 2017. The telemetry data in MongoDB has more than 500 attributes. The application changes the attribute names when the data is moved to SQL Server 2017.
The database structure contains both OLAP and OLTP databases.
Mechanical Workflow
Mechanical Workflow is used to track changes and improvements made to the cars during their lifetime.
Currently, Mechanical Workflow runs on SQL Server 2017 as an OLAP system.
Mechanical Workflow has a named Table1 that is 1 TB. Large aggregations are performed on a single column of Table 1.
Requirements
Planned Changes
Litware is the process of rearchitecting its data estate to be hosted in Azure. The company plans to decommission the London datacentre and move all its applications to an Azure datacentre.
Technical Requirements
Litware identifies the following technical requirements:
Data collection for Race Central must be moved to Azure Cosmos DB and Azure SQL Database. The data must be written to the Azure datacentre closest to each race and must converge in the least amount of time.
The query performance of Race Central must be stable, and the administrative time it takes to perform optimizations must be minimized.
The datacentre for Mechanical Workflow must be moved to Azure SQL data Warehouse.
Transparent data encryption (IDE) must be enabled on all data stores, whenever possible.
An Azure Data Factory pipeline must be used to move data from Cosmos DB to SQL Database for Race Central. If the data load takes longer than 20 minutes, configuration changes must be made to Data Factory.
The telemetry data must migrate toward a solution that is native to Azure.
The telemetry data must be monitored for performance issues. You must adjust the Cosmos DB Request Units per second (RU/s) to maintain a performance SLA while minimizing the cost of the Ru/s.
Data Masking Requirements
During rare weekends, visitors will be able to enter the remote portable offices. Litware is concerned that some proprietary information might be exposed. The company identifies the following data masking requirements for the Race Central data that will be stored in SQL Database:
Only show the last four digits of the values in a column named SuspensionSprings.
Only Show a zero value for the values in a column named ShockOilWeight.
Which two metrics should you use to identify the appropriate RU/s for the telemetry data? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

A. Number of requests
B. Number of requests exceeded capacity
C. End to end observed read latency at the 99th percentile
D. Session consistency
E. Data + Index storage consumed
F. Avg Troughput/s

Answer: AE
Explanation:
Scenario: The telemetry data must be monitored for performance issues. You must adjust the Cosmos DB Request Units per second (RU/s) to maintain a performance SLA while minimizing the cost of the Ru/s.
With Azure Cosmos DB, you pay for the throughput you provision and the storage you consume on an hourly basis.
While you estimate the number of RUs per second to provision, consider the following factors:
Item size: As the size of an item increases, the number of RUs consumed to read or write the item also increases.

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