Implementing an Azure Data Solution v1.0 (DP-200)

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Total 241 questions

Background -
Proseware, Inc, develops and manages a product named Poll Taker. The product is used for delivering public opinion polling and analysis.
Polling data comes from a variety of sources, including online surveys, house-to-house interviews, and booths at public events.

Polling data -
Polling data is stored in one of the two locations:
An on-premises Microsoft SQL Server 2019 database named PollingData
Azure Data Lake Gen 2
Data in Data Lake is queried by using PolyBase

Poll metadata -
Each poll has associated metadata with information about the poll including the date and number of respondents. The data is stored as JSON.

Phone-based polling -

Security -
Phone-based poll data must only be uploaded by authorized users from authorized devices
Contractors must not have access to any polling data other than their own
Access to polling data must set on a per-active directory user basis

Data migration and loading -
All data migration processes must use Azure Data Factory
All data migrations must run automatically during non-business hours
Data migrations must be reliable and retry when needed

Performance -
After six months, raw polling data should be moved to a storage account. The storage must be available in the event of a regional disaster. The solution must minimize costs.

Deployments -
All deployments must be performed by using Azure DevOps. Deployments must use templates used in multiple environments
No credentials or secrets should be used during deployments

Reliability -
All services and processes must be resilient to a regional Azure outage.

Monitoring -
All Azure services must be monitored by using Azure Monitor. On-premises SQL Server performance must be monitored.


DRAG DROP -
You need to ensure that phone-based polling data can be analyzed in the PollingData database.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer are and arrange them in the correct order.
Select and Place:




Answer :

Explanation:
Scenario:
All deployments must be performed by using Azure DevOps. Deployments must use templates used in multiple environments
No credentials or secrets should be used during deployments

Background -
Proseware, Inc, develops and manages a product named Poll Taker. The product is used for delivering public opinion polling and analysis.
Polling data comes from a variety of sources, including online surveys, house-to-house interviews, and booths at public events.

Polling data -
Polling data is stored in one of the two locations:
An on-premises Microsoft SQL Server 2019 database named PollingData
Azure Data Lake Gen 2
Data in Data Lake is queried by using PolyBase

Poll metadata -
Each poll has associated metadata with information about the poll including the date and number of respondents. The data is stored as JSON.

Phone-based polling -

Security -
Phone-based poll data must only be uploaded by authorized users from authorized devices
Contractors must not have access to any polling data other than their own
Access to polling data must set on a per-active directory user basis

Data migration and loading -
All data migration processes must use Azure Data Factory
All data migrations must run automatically during non-business hours
Data migrations must be reliable and retry when needed

Performance -
After six months, raw polling data should be moved to a storage account. The storage must be available in the event of a regional disaster. The solution must minimize costs.

Deployments -
All deployments must be performed by using Azure DevOps. Deployments must use templates used in multiple environments
No credentials or secrets should be used during deployments

Reliability -
All services and processes must be resilient to a regional Azure outage.

Monitoring -
All Azure services must be monitored by using Azure Monitor. On-premises SQL Server performance must be monitored.


DRAG DROP -
You need to provision the polling data storage account.
How should you configure the storage account? To answer, drag the appropriate Configuration Value to the correct Setting. Each Configuration 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.
Select and Place:




Answer :

Explanation:

Account type: StorageV2 -
You must create new storage accounts as type StorageV2 (general-purpose V2) to take advantage of Data Lake Storage Gen2 features.
Scenario: Polling data is stored in one of the two locations:
✑ An on-premises Microsoft SQL Server 2019 database named PollingData
✑ Azure Data Lake Gen 2
Data in Data Lake is queried by using PolyBase

Replication type: RA-GRS -
Scenario: All services and processes must be resilient to a regional Azure outage.
Geo-redundant storage (GRS) is designed to provide at least 99.99999999999999% (16 9's) durability of objects over a given year by replicating your data to a secondary region that is hundreds of miles away from the primary region. If your storage account has GRS enabled, then your data is durable even in the case of a complete regional outage or a disaster in which the primary region isn't recoverable.
If you opt for GRS, you have two related options to choose from:
✑ GRS replicates your data to another data center in a secondary region, but that data is available to be read only if Microsoft initiates a failover from the primary to secondary region.
✑ Read-access geo-redundant storage (RA-GRS) is based on GRS. RA-GRS replicates your data to another data center in a secondary region, and also provides you with the option to read from the secondary region. With RA-GRS, you can read from the secondary region regardless of whether Microsoft initiates a failover from the primary to secondary region.
References:
https://docs.microsoft.com/bs-cyrl-ba/azure/storage/blobs/data-lake-storage-quickstart-create-account https://docs.microsoft.com/en-us/azure/storage/common/storage-redundancy-grs
Implement data storage solutions

Case Study -
This is a case study. Case studies are not timed separately. You can use as much exam time as you would like to complete each case. However, there may be additional case studies and sections on this exam. You must manage your time to ensure that you are able to complete all questions included on this exam in the time provided.
To answer the questions included in a case study, you will need to reference information that is provided in the case study. Case studies might contain exhibits and other resources that provide more information about the scenario that is described in the case study. Each question is independent of the other questions in this case study.
At the end of this case study, a review screen will appear. This screen allows you to review your answers and to make changes before you move to the next section of the exam. After you begin a new section, you cannot return to this section.

To start the case study -
To display the first question in this case study, click the Next button. Use the buttons in the left pane to explore the content of the case study before you answer the questions. Clicking these buttons displays information such as business requirements, existing environment, and problem statements. If the case study has an All Information tab, note that the information displayed is identical to the information displayed on the subsequent tabs. When you are ready to answer a question, click the Question button to return to the question.

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 datacenter 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 table named Table1 that is 1 TB. Large aggregations are performed on a single column of Table1.

Requirements -

Planned Changes -
Litware is in 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 datacenter.

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 datacenter 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 database for Mechanical Workflow must be moved to Azure Synapse Analytics.
Transparent data encryption (TDE) 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 race 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.


HOTSPOT -
You need to build a solution to collect the telemetry data for Race Central.
What should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Hot Area:




Answer :

Explanation:

API: Table -
Azure Cosmos DB provides native support for wire protocol-compatible APIs for popular databases. These include MongoDB, Apache Cassandra, Gremlin, and
Azure Table storage.
Scenario: The telemetry data must migrate toward a solution that is native to Azure.

Consistency level: Strong -
Use the strongest consistency Strong to minimize convergence time.
Scenario: The data must be written to the Azure datacenter closest to each race and must converge in the least amount of time.
Reference:
https://docs.microsoft.com/en-us/azure/cosmos-db/consistency-levels

Case Study -
This is a case study. Case studies are not timed separately. You can use as much exam time as you would like to complete each case. However, there may be additional case studies and sections on this exam. You must manage your time to ensure that you are able to complete all questions included on this exam in the time provided.
To answer the questions included in a case study, you will need to reference information that is provided in the case study. Case studies might contain exhibits and other resources that provide more information about the scenario that is described in the case study. Each question is independent of the other questions in this case study.
At the end of this case study, a review screen will appear. This screen allows you to review your answers and to make changes before you move to the next section of the exam. After you begin a new section, you cannot return to this section.

To start the case study -
To display the first question in this case study, click the Next button. Use the buttons in the left pane to explore the content of the case study before you answer the questions. Clicking these buttons displays information such as business requirements, existing environment, and problem statements. If the case study has an All Information tab, note that the information displayed is identical to the information displayed on the subsequent tabs. When you are ready to answer a question, click the Question button to return to the question.

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 datacenter 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 table named Table1 that is 1 TB. Large aggregations are performed on a single column of Table1.

Requirements -

Planned Changes -
Litware is in 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 datacenter.

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 datacenter 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 database for Mechanical Workflow must be moved to Azure Synapse Analytics.
Transparent data encryption (TDE) 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 race 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.

On which data store should you configure TDE to meet the technical requirements?

  • A. Cosmos DB
  • B. Azure Synapse Analytics
  • C. Azure SQL Database


Answer : B

Explanation:
Scenario: Transparent data encryption (TDE) must be enabled on all data stores, whenever possible.
The database for Mechanical Workflow must be moved to Azure Synapse Analytics.
Incorrect Answers:
A: Cosmos DB does not support TDE.

Case Study -
This is a case study. Case studies are not timed separately. You can use as much exam time as you would like to complete each case. However, there may be additional case studies and sections on this exam. You must manage your time to ensure that you are able to complete all questions included on this exam in the time provided.
To answer the questions included in a case study, you will need to reference information that is provided in the case study. Case studies might contain exhibits and other resources that provide more information about the scenario that is described in the case study. Each question is independent of the other questions in this case study.
At the end of this case study, a review screen will appear. This screen allows you to review your answers and to make changes before you move to the next section of the exam. After you begin a new section, you cannot return to this section.

To start the case study -
To display the first question in this case study, click the Next button. Use the buttons in the left pane to explore the content of the case study before you answer the questions. Clicking these buttons displays information such as business requirements, existing environment, and problem statements. If the case study has an All Information tab, note that the information displayed is identical to the information displayed on the subsequent tabs. When you are ready to answer a question, click the Question button to return to the question.

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 datacenter 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 table named Table1 that is 1 TB. Large aggregations are performed on a single column of Table1.

Requirements -

Planned Changes -
Litware is in 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 datacenter.

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 datacenter 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 database for Mechanical Workflow must be moved to Azure Synapse Analytics.
Transparent data encryption (TDE) 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 race 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.


HOTSPOT -
You are building the data store solution for Mechanical Workflow.
How should you configure Table1? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Hot Area:




Answer :

Table Type: Hash distributed.
Hash-distributed tables improve query performance on large fact tables.

Index type: Clusted columnstore -
Scenario:
Mechanical Workflow has a named Table1 that is 1 TB. Large aggregations are performed on a single column of Table 1.
References:
https://docs.microsoft.com/en-us/azure/sql-data-warehouse/sql-data-warehouse-tables-distribute

Case Study -
This is a case study. Case studies are not timed separately. You can use as much exam time as you would like to complete each case. However, there may be additional case studies and sections on this exam. You must manage your time to ensure that you are able to complete all questions included on this exam in the time provided.
To answer the questions included in a case study, you will need to reference information that is provided in the case study. Case studies might contain exhibits and other resources that provide more information about the scenario that is described in the case study. Each question is independent of the other questions in this case study.
At the end of this case study, a review screen will appear. This screen allows you to review your answers and to make changes before you move to the next section of the exam. After you begin a new section, you cannot return to this section.

To start the case study -
To display the first question in this case study, click the Next button. Use the buttons in the left pane to explore the content of the case study before you answer the questions. Clicking these buttons displays information such as business requirements, existing environment, and problem statements. If the case study has an All Information tab, note that the information displayed is identical to the information displayed on the subsequent tabs. When you are ready to answer a question, click the Question button to return to the question.

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 datacenter 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 table named Table1 that is 1 TB. Large aggregations are performed on a single column of Table1.

Requirements -

Planned Changes -
Litware is in 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 datacenter.

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 datacenter 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 database for Mechanical Workflow must be moved to Azure Synapse Analytics.
Transparent data encryption (TDE) 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 race 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.


HOTSPOT -
Which masking functions should you implement for each column to meet the data masking requirements? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Hot Area:




Answer :

Explanation:

Box 1: Credit Card -
The Credit Card Masking method exposes the last four digits of the designated fields and adds a constant string as a prefix in the form of a credit card.

Example: XXXX-XXXX-XXXX-1234 -
✑ Only show the last four digits of the values in a column named SuspensionSprings.

Box 2: Default -
Default uses a zero value for numeric data types (bigint, bit, decimal, int, money, numeric, smallint, smallmoney, tinyint, float, real).
✑ Only show a zero value for the values in a column named ShockOilWeight.
Scenario:
The company identifies the following data masking requirements for the Race Central data that will be stored in SQL Database:
✑ Only show a zero value for the values in a column named ShockOilWeight.
✑ Only show the last four digits of the values in a column named SuspensionSprings.
Reference:
https://docs.microsoft.com/en-us/azure/azure-sql/database/dynamic-data-masking-overview

Case Study -
This is a case study. Case studies are not timed separately. You can use as much exam time as you would like to complete each case. However, there may be additional case studies and sections on this exam. You must manage your time to ensure that you are able to complete all questions included on this exam in the time provided.
To answer the questions included in a case study, you will need to reference information that is provided in the case study. Case studies might contain exhibits and other resources that provide more information about the scenario that is described in the case study. Each question is independent of the other questions in this case study.
At the end of this case study, a review screen will appear. This screen allows you to review your answers and to make changes before you move to the next section of the exam. After you begin a new section, you cannot return to this section.

To start the case study -
To display the first question in this case study, click the Next button. Use the buttons in the left pane to explore the content of the case study before you answer the questions. Clicking these buttons displays information such as business requirements, existing environment, and problem statements. If the case study has an All Information tab, note that the information displayed is identical to the information displayed on the subsequent tabs. When you are ready to answer a question, click the Question button to return to the question.

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 datacenter 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 table named Table1 that is 1 TB. Large aggregations are performed on a single column of Table1.

Requirements -

Planned Changes -
Litware is in 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 datacenter.

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 datacenter 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 database for Mechanical Workflow must be moved to Azure Synapse Analytics.
Transparent data encryption (TDE) 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 race 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.


HOTSPOT -
Which masking functions should you implement for each column to meet the data masking requirements? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Hot Area:




Answer :

Explanation:
Box 1: Custom text/string: A masking method, which exposes the first and/or last characters and adds a custom padding string in the middle.
Only show the last four digits of the values in a column named SuspensionSprings.

Box 2: Default -
Default uses a zero value for numeric data types (bigint, bit, decimal, int, money, numeric, smallint, smallmoney, tinyint, float, real).
Scenario: Only show a zero value for the values in a column named ShockOilWeight.
Scenario:
The company identifies the following data masking requirements for the Race Central data that will be stored in SQL Database:
✑ Only show a zero value for the values in a column named ShockOilWeight.
✑ Only show the last four digits of the values in a column named SuspensionSprings.
Reference:
https://docs.microsoft.com/en-us/azure/azure-sql/database/dynamic-data-masking-overview
Implement data storage solutions

Case study -
This is a case study. Case studies are not timed separately. You can use as much exam time as you would like to complete each case. However, there may be additional case studies and sections on this exam. You must manage your time to ensure that you are able to complete all questions included on this exam in the time provided.
To answer the questions included in a case study, you will need to reference information that is provided in the case study. Case studies might contain exhibits and other resources that provide more information about the scenario that is described in the case study. Each question is independent of the other questions in this case study.
At the end of this case study, a review screen will appear. This screen allows you to review your answers and to make changes before you move to the next section of the exam. After you begin a new section, you cannot return to this section.

To start the case study -
To display the first question in this case study, click the Next button. Use the buttons in the left pane to explore the content of the case study before you answer the questions. Clicking these buttons displays information such as business requirements, existing environment, and problem statements. If the case study has an
All Information tab, note that the information displayed is identical to the information displayed on the subsequent tabs. When you are ready to answer a question, click the Question button to return to the question.

Overview -
ADatum Corporation is a retailer that sells products through two sales channels: retail stores and a website.

Existing Environment -
ADatum has one database server that has Microsoft SQL Server 2016 installed. The server hosts three mission-critical databases named SALESDB, DOCDB, and REPORTINGDB.
SALESDB collects data from the stored and the website.
DOCDB stored documents that connect to the sales data in SALESDB. The documents are stored in two different JSON formats based on the sales channel.
REPORTINGDB stores reporting data and contains server columnstore indexes. A daily process creates reporting data in REPORTINGDB from the data in
SALESDB. The process is implemented as a SQL Server Integration Services (SSIS) package that runs a stored procedure from SALESDB.

Requirements -

Planned Changes -
ADatum plans to move the current data infrastructure to Azure. The new infrastructure has the following requirements:
Migrate SALESDB and REPORTINGDB to an Azure SQL database.
Migrate DOCDB to Azure Cosmos DB.
The sales data, including the documents in JSON format, must be gathered as it arrives and analyzed online by using Azure Stream Analytics. The analytic process will perform aggregations that must be done continuously, without gaps, and without overlapping.
As they arrive, all the sales documents in JSON format must be transformed into one consistent format.
Azure Data Factory will replace the SSIS process of copying the data from SALESDB to REPORTINGDB.

Technical Requirements -
The new Azure data infrastructure must meet the following technical requirements:
Data in SALESDB must encrypted by using Transparent Data Encryption (TDE). The encryption must use your own key.
SALESDB must be restorable to any given minute within the past three weeks.
Real-time processing must be monitored to ensure that workloads are sized properly based on actual usage patterns.
Missing indexes must be created automatically for REPORTINGDB.
Disk IO, CPU, and memory usage must be monitored for SALESDB.

You need to configure a disaster recovery solution for SALESDB to meet the technical requirements.
What should you configure in the backup policy?

  • A. weekly long-term retention backups that are retained for three weeks
  • B. failover groups
  • C. a point-in-time restore
  • D. geo-replication


Answer : C

Explanation:
Scenario: SALESDB must be restorable to any given minute within the past three weeks.
The Azure SQL Database service protects all databases with an automated backup system. These backups are retained for 7 days for Basic, 35 days for
Standard and 35 days for Premium. Point-in-time restore is a self-service capability, allowing customers to restore a Basic, Standard or Premium database from these backups to any point within the retention period.
References:
https://azure.microsoft.com/en-us/blog/azure-sql-database-point-in-time-restore/

Case study -
This is a case study. Case studies are not timed separately. You can use as much exam time as you would like to complete each case. However, there may be additional case studies and sections on this exam. You must manage your time to ensure that you are able to complete all questions included on this exam in the time provided.
To answer the questions included in a case study, you will need to reference information that is provided in the case study. Case studies might contain exhibits and other resources that provide more information about the scenario that is described in the case study. Each question is independent of the other questions in this case study.
At the end of this case study, a review screen will appear. This screen allows you to review your answers and to make changes before you move to the next section of the exam. After you begin a new section, you cannot return to this section.

To start the case study -
To display the first question in this case study, click the Next button. Use the buttons in the left pane to explore the content of the case study before you answer the questions. Clicking these buttons displays information such as business requirements, existing environment, and problem statements. If the case study has an
All Information tab, note that the information displayed is identical to the information displayed on the subsequent tabs. When you are ready to answer a question, click the Question button to return to the question.

Overview -
ADatum Corporation is a retailer that sells products through two sales channels: retail stores and a website.

Existing Environment -
ADatum has one database server that has Microsoft SQL Server 2016 installed. The server hosts three mission-critical databases named SALESDB, DOCDB, and REPORTINGDB.
SALESDB collects data from the stored and the website.
DOCDB stored documents that connect to the sales data in SALESDB. The documents are stored in two different JSON formats based on the sales channel.
REPORTINGDB stores reporting data and contains server columnstore indexes. A daily process creates reporting data in REPORTINGDB from the data in
SALESDB. The process is implemented as a SQL Server Integration Services (SSIS) package that runs a stored procedure from SALESDB.

Requirements -

Planned Changes -
ADatum plans to move the current data infrastructure to Azure. The new infrastructure has the following requirements:
Migrate SALESDB and REPORTINGDB to an Azure SQL database.
Migrate DOCDB to Azure Cosmos DB.
The sales data, including the documents in JSON format, must be gathered as it arrives and analyzed online by using Azure Stream Analytics. The analytic process will perform aggregations that must be done continuously, without gaps, and without overlapping.
As they arrive, all the sales documents in JSON format must be transformed into one consistent format.
Azure Data Factory will replace the SSIS process of copying the data from SALESDB to REPORTINGDB.

Technical Requirements -
The new Azure data infrastructure must meet the following technical requirements:
Data in SALESDB must encrypted by using Transparent Data Encryption (TDE). The encryption must use your own key.
SALESDB must be restorable to any given minute within the past three weeks.
Real-time processing must be monitored to ensure that workloads are sized properly based on actual usage patterns.
Missing indexes must be created automatically for REPORTINGDB.
Disk IO, CPU, and memory usage must be monitored for SALESDB.

You need to implement event processing by using Stream Analytics to produce consistent JSON documents.
Which three actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

  • A. Define an output to Cosmos DB.
  • B. Define a query that contains a JavaScript user-defined aggregates (UDA) function.
  • C. Define a reference input.
  • D. Define a transformation query.
  • E. Define an output to Azure Data Lake Storage Gen2.
  • F. Define a stream input.


Answer : DEF

Explanation:
✑ DOCDB stored documents that connect to the sales data in SALESDB. The documents are stored in two different JSON formats based on the sales channel.
✑ The sales data, including the documents in JSON format, must be gathered as it arrives and analyzed online by using Azure Stream Analytics. The analytic process will perform aggregations that must be done continuously, without gaps, and without overlapping.
As they arrive, all the sales documents in JSON format must be transformed into one consistent format.


Manage and develop data processing

Note: This question is a part of series of questions that present the same scenario. Each question in the series contains a unique solution. Determine whether the solution meets the stated goals.
You develop a data ingestion process that will import data to an enterprise data warehouse in Azure Synapse Analytics. The data to be ingested resides in parquet files stored in an Azure Data Lake Gen 2 storage account.
You need to load the data from the Azure Data Lake Gen 2 storage account into the Data Warehouse.
Solution:
1. Use Azure Data Factory to convert the parquet files to CSV files
2. Create an external data source pointing to the Azure storage account
3. Create an external file format and external table using the external data source
4. Load the data using the INSERTג€¦SELECT statement
Does the solution meet the goal?

  • A. Yes
  • B. No


Answer : B

Explanation:
There is no need to convert the parquet files to CSV files.
You load the data using the CREATE TABLE AS SELECT statement.
References:
https://docs.microsoft.com/en-us/azure/sql-data-warehouse/sql-data-warehouse-load-from-azure-data-lake-store

Note: This question is a part of series of questions that present the same scenario. Each question in the series contains a unique solution. Determine whether the solution meets the stated goals.
You develop a data ingestion process that will import data to an enterprise data warehouse in Azure Synapse Analytics. The data to be ingested resides in parquet files stored in an Azure Data Lake Gen 2 storage account.
You need to load the data from the Azure Data Lake Gen 2 storage account into the Data Warehouse.
Solution:
1. Create an external data source pointing to the Azure storage account
2. Create an external file format and external table using the external data source
3. Load the data using the INSERTג€¦SELECT statement
Does the solution meet the goal?

  • A. Yes
  • B. No


Answer : B

Explanation:
You load the data using the CREATE TABLE AS SELECT statement.
References:
https://docs.microsoft.com/en-us/azure/sql-data-warehouse/sql-data-warehouse-load-from-azure-data-lake-store

Note: This question is a part of series of questions that present the same scenario. Each question in the series contains a unique solution. Determine whether the solution meets the stated goals.
You develop a data ingestion process that will import data to an enterprise data warehouse in Azure Synapse Analytics. The data to be ingested resides in parquet files stored in an Azure Data Lake Gen 2 storage account.
You need to load the data from the Azure Data Lake Gen 2 storage account into the Data Warehouse.
Solution:
1. Create an external data source pointing to the Azure storage account
2. Create a workload group using the Azure storage account name as the pool name
3. Load the data using the INSERTג€¦SELECT statement
Does the solution meet the goal?

  • A. Yes
  • B. No


Answer : B

Explanation:
You need to create an external file format and external table using the external data source.
You then load the data using the CREATE TABLE AS SELECT statement.
References:
https://docs.microsoft.com/en-us/azure/sql-data-warehouse/sql-data-warehouse-load-from-azure-data-lake-store

You develop data engineering solutions for a company.
You must integrate the companyג€™s on-premises Microsoft SQL Server data with Microsoft Azure SQL Database. Data must be transformed incrementally.
You need to implement the data integration solution.
Which tool should you use to configure a pipeline to copy data?

  • A. Use the Copy Data tool with Blob storage linked service as the source
  • B. Use Azure PowerShell with SQL Server linked service as a source
  • C. Use Azure Data Factory UI with Blob storage linked service as a source
  • D. Use the .NET Data Factory API with Blob storage linked service as the source


Answer : C

Explanation:
The Integration Runtime is a customer managed data integration infrastructure used by Azure Data Factory to provide data integration capabilities across different network environments.
A linked service defines the information needed for Azure Data Factory to connect to a data resource. We have three resources in this scenario for which linked services are needed:
✑ On-premises SQL Server
✑ Azure Blob Storage
✑ Azure SQL database
Note: Azure Data Factory is a fully managed cloud-based data integration service that orchestrates and automates the movement and transformation of data. The key concept in the ADF model is pipeline. A pipeline is a logical grouping of Activities, each of which defines the actions to perform on the data contained in
Datasets. Linked services are used to define the information needed for Data Factory to connect to the data resources.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/team-data-science-process/move-sql-azure-adf

HOTSPOT -
A company runs Microsoft Dynamics CRM with Microsoft SQL Server on-premises. SQL Server Integration Services (SSIS) packages extract data from Dynamics
CRM APIs, and load the data into a SQL Server data warehouse.
The datacenter is running out of capacity. Because of the network configuration, you must extract on premises data to the cloud over https. You cannot open any additional ports. The solution must implement the least amount of effort.
You need to create the pipeline system.
Which component should you use? To answer, select the appropriate technology in the dialog box in the answer area.
NOTE: Each correct selection is worth one point.
Hot Area:




Answer :

Explanation:

Box 1: Source -
For Copy activity, it requires source and sink linked services to define the direction of data flow.
Copying between a cloud data source and a data source in private network: if either source or sink linked service points to a self-hosted IR, the copy activity is executed on that self-hosted Integration Runtime.
Box 2: Self-hosted integration runtime
A self-hosted integration runtime can run copy activities between a cloud data store and a data store in a private network, and it can dispatch transform activities against compute resources in an on-premises network or an Azure virtual network. The installation of a self-hosted integration runtime needs on an on-premises machine or a virtual machine (VM) inside a private network.
References:
https://docs.microsoft.com/en-us/azure/data-factory/create-self-hosted-integration-runtime

DRAG DROP -
You develop data engineering solutions for a company.
A project requires analysis of real-time Twitter feeds. Posts that contain specific keywords must be stored and processed on Microsoft Azure and then displayed by using Microsoft Power BI. You need to implement the solution.
Which five actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Select and Place:




Answer :

Explanation:
Step 1: Create an HDInisght cluster with the Spark cluster type
Step 2: Create a Jyputer Notebook

Step 3: Create a table -
The Jupyter Notebook that you created in the previous step includes code to create an hvac table.
Step 4: Run a job that uses the Spark Streaming API to ingest data from Twitter
Step 5: Load the hvac table into Power BI Desktop
You use Power BI to create visualizations, reports, and dashboards from the Spark cluster data.
References:
https://acadgild.com/blog/streaming-twitter-data-using-spark
https://docs.microsoft.com/en-us/azure/hdinsight/spark/apache-spark-use-with-data-lake-store

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