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**** DP-203 Description | DP-203 Syllabus | DP-203 Exam Objectives | DP-203 Course Outline ****
**** SAMPLE Data Engineering on Microsoft Azure 2021 Dumps ****
Question: 1 Section 6
Introductory Info 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 -
Litware, Inc. owns and operates 300 convenience stores across the US. The company sells a variety of packaged foods and drinks, as well as a variety of prepared
foods, such as sandwiches and pizzas.
Litware has a loyalty club whereby members can get daily discounts on specific items by providing their membership number at checkout.
Litware employs business analysts who prefer to analyze data by using Microsoft Power BI, and data scientists who prefer analyzing data in Azure Databricks
notebooks.
Requirements -
Business Goals -
Litware wants to create a new analytics environment in Azure to meet the following requirements:
See inventory levels across the stores. Data must be updated as close to real time as possible.
Execute ad hoc analytical queries on historical data to identify whether the loyalty club discounts increase sales of the discounted products.
Every four hours, notify store employees about how many prepared food items to produce based on historical demand from the sales data.
Technical Requirements -
Litware identifies the following technical requirements:
Minimize the number of different Azure services needed to achieve the business goals.
Use platform as a service (PaaS) offerings whenever possible and avoid having to provision virtual machines that must be managed by Litware.
Ensure that the analytical data store is accessible only to the company's on-premises network and Azure services.
Use Azure Active Directory (Azure AD) authentication whenever possible.
Use the principle of least privilege when designing security.
Stage Inventory data in Azure Data Lake Storage Gen2 before loading the data into the analytical data store. Litware wants to remove transient data from Data
Lake Storage once the data is no longer in use. Files that have a modified date that is older than 14 days must be removed.
Limit the business analysts' access to customer contact information, such as phone numbers, because this type of data is not analytically relevant.
Ensure that you can quickly restore a copy of the analytical data store within one hour in the event of corruption or accidental deletion.
Planned Environment -
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Litware plans to implement the following environment:
The application development team will create an Azure event hub to receive real-time sales data, including store number, date, time, product ID, customer loyalty
number, price, and discount amount, from the point of sale (POS) system and output the data to data storage in Azure.
Customer data, including name, contact information, and loyalty number, comes from Salesforce, a SaaS application, and can be imported into Azure once every
eight hours. Row modified dates are not trusted in the source table.
Product data, including product ID, name, and category, comes from Salesforce and can be imported into Azure once every eight hours. Row modified dates are not
trusted in the source table.
Daily inventory data comes from a Microsoft SQL server located on a private network.
Litware currently has 5 TB of historical sales data and 100 GB of customer data. The company expects approximately 100 GB of new data per month for the next
year.
Litware will build a custom application named FoodPrep to provide store employees with the calculation results of how many prepared food items to produce every
four hours.
Litware does not plan to implement Azure ExpressRoute or a VPN between the on-premises network and Azure. Question HOTSPOT -
Which Azure Data Factory components should you recommend using together to import the daily inventory data from the SQL server to Azure Data Lake Storage?
To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Hot Area:
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Answer:
Box 1: Self-hosted integration runtime
A self-hosted IR is capable of running copy activity between a cloud data stores and a data store in private network.
Box 2: Schedule trigger -
Schedule every 8 hours -
Box 3: Copy activity -
Scenario:
ԣ� Customer data, including name, contact information, and loyalty number, comes from Salesforce and can be imported into Azure once every eight hours. Row
modified dates are not trusted in the source table.
ԣ� Product data, including product ID, name, and category, comes from Salesforce and can be imported into Azure once every eight hours. Row modified dates
are not trusted in the source table.
Design and develop data processing
Question: 1 Section 7
Introductory Info 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
$13$10
the Question button to return to the question.
Overview -
Litware, Inc. owns and operates 300 convenience stores across the US. The company sells a variety of packaged foods and drinks, as well as a variety of prepared
foods, such as sandwiches and pizzas.
Litware has a loyalty club whereby members can get daily discounts on specific items by providing their membership number at checkout.
Litware employs business analysts who prefer to analyze data by using Microsoft Power BI, and data scientists who prefer analyzing data in Azure Databricks
notebooks.
Requirements -
Business Goals -
Litware wants to create a new analytics environment in Azure to meet the following requirements:
See inventory levels across the stores. Data must be updated as close to real time as possible.
Execute ad hoc analytical queries on historical data to identify whether the loyalty club discounts increase sales of the discounted products.
Every four hours, notify store employees about how many prepared food items to produce based on historical demand from the sales data.
Technical Requirements -
Litware identifies the following technical requirements:
Minimize the number of different Azure services needed to achieve the business goals.
Use platform as a service (PaaS) offerings whenever possible and avoid having to provision virtual machines that must be managed by Litware.
Ensure that the analytical data store is accessible only to the company's on-premises network and Azure services.
Use Azure Active Directory (Azure AD) authentication whenever possible.
Use the principle of least privilege when designing security.
Stage Inventory data in Azure Data Lake Storage Gen2 before loading the data into the analytical data store. Litware wants to remove transient data from Data
Lake Storage once the data is no longer in use. Files that have a modified date that is older than 14 days must be removed.
Limit the business analysts' access to customer contact information, such as phone numbers, because this type of data is not analytically relevant.
Ensure that you can quickly restore a copy of the analytical data store within one hour in the event of corruption or accidental deletion.
Planned Environment -
Litware plans to implement the following environment:
The application development team will create an Azure event hub to receive real-time sales data, including store number, date, time, product ID, customer loyalty
number, price, and discount amount, from the point of sale (POS) system and output the data to data storage in Azure.
Customer data, including name, contact information, and loyalty number, comes from Salesforce, a SaaS application, and can be imported into Azure once every
eight hours. Row modified dates are not trusted in the source table.
Product data, including product ID, name, and category, comes from Salesforce and can be imported into Azure once every eight hours. Row modified dates are not
trusted in the source table.
Daily inventory data comes from a Microsoft SQL server located on a private network.
Litware currently has 5 TB of historical sales data and 100 GB of customer data. The company expects approximately 100 GB of new data per month for the next
year.
Litware will build a custom application named FoodPrep to provide store employees with the calculation results of how many prepared food items to produce every
four hours.
Litware does not plan to implement Azure ExpressRoute or a VPN between the on-premises network and Azure. Question What should you recommend to prevent
users outside the Litware on-premises network from accessing the analytical data store?
A. a server-level virtual network rule
B. a database-level virtual network rule
C. a server-level firewall IP rule
D. a database-level firewall IP rule
Answer: A
Scenario: Ensure that the analytical data store is accessible only to the companys on-premises network and Azure services.
Virtual network rules are one firewall security feature that controls whether the database server for your single databases and elastic pool in Azure SQL Database or
for your databases in SQL Data Warehouse accepts communications that are sent from particular subnets in virtual networks.
Server-level, not database-level: Each virtual network rule applies to your whole Azure SQL Database server, not just to one particular database on the server. In
other words, virtual network rule applies at the server-level, not at the database-level.
Reference:
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https://docs.microsoft.com/en-us/azure/sql-database/sql-database-vnet-service-endpoint-rule-overview
Question: 2 Section 7
Introductory Info 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 -
Litware, Inc. owns and operates 300 convenience stores across the US. The company sells a variety of packaged foods and drinks, as well as a variety of prepared
foods, such as sandwiches and pizzas.
Litware has a loyalty club whereby members can get daily discounts on specific items by providing their membership number at checkout.
Litware employs business analysts who prefer to analyze data by using Microsoft Power BI, and data scientists who prefer analyzing data in Azure Databricks
notebooks.
Requirements -
Business Goals -
Litware wants to create a new analytics environment in Azure to meet the following requirements:
See inventory levels across the stores. Data must be updated as close to real time as possible.
Execute ad hoc analytical queries on historical data to identify whether the loyalty club discounts increase sales of the discounted products.
Every four hours, notify store employees about how many prepared food items to produce based on historical demand from the sales data.
Technical Requirements -
Litware identifies the following technical requirements:
Minimize the number of different Azure services needed to achieve the business goals.
Use platform as a service (PaaS) offerings whenever possible and avoid having to provision virtual machines that must be managed by Litware.
Ensure that the analytical data store is accessible only to the company's on-premises network and Azure services.
Use Azure Active Directory (Azure AD) authentication whenever possible.
Use the principle of least privilege when designing security.
Stage Inventory data in Azure Data Lake Storage Gen2 before loading the data into the analytical data store. Litware wants to remove transient data from Data
Lake Storage once the data is no longer in use. Files that have a modified date that is older than 14 days must be removed.
Limit the business analysts' access to customer contact information, such as phone numbers, because this type of data is not analytically relevant.
Ensure that you can quickly restore a copy of the analytical data store within one hour in the event of corruption or accidental deletion.
Planned Environment -
Litware plans to implement the following environment:
The application development team will create an Azure event hub to receive real-time sales data, including store number, date, time, product ID, customer loyalty
number, price, and discount amount, from the point of sale (POS) system and output the data to data storage in Azure.
Customer data, including name, contact information, and loyalty number, comes from Salesforce, a SaaS application, and can be imported into Azure once every
eight hours. Row modified dates are not trusted in the source table.
Product data, including product ID, name, and category, comes from Salesforce and can be imported into Azure once every eight hours. Row modified dates are not
trusted in the source table.
Daily inventory data comes from a Microsoft SQL server located on a private network.
Litware currently has 5 TB of historical sales data and 100 GB of customer data. The company expects approximately 100 GB of new data per month for the next
$13$10
year.
Litware will build a custom application named FoodPrep to provide store employees with the calculation results of how many prepared food items to produce every
four hours.
Litware does not plan to implement Azure ExpressRoute or a VPN between the on-premises network and Azure. Question What should you recommend using to
secure sensitive customer contact information?
A. Transparent Data Encryption (TDE)
B. row-level security
C. column-level security
D. data sensitivity labels
Answer: D
Scenario: Limit the business analysts access to customer contact information, such as phone numbers, because this type of data is not analytically relevant.
Labeling: You can apply sensitivity-classification labels persistently to columns by using new metadata attributes that have been added to the SQL Server database
engine. This metadata can then be used for advanced, sensitivity-based auditing and protection scenarios.
Incorrect Answers:
A: Transparent Data Encryption (TDE) encrypts SQL Server, Azure SQL Database, and Azure Synapse Analytics data files, known as encrypting data at rest. TDE
does not provide encryption across communication channels.
Reference:
https://docs.microsoft.com/en-us/azure/azure-sql/database/data-discovery-and-classification-overview https://docs.microsoft.com/en-us/azure/sql-database/sql-
database-security-overview
Design and implement data security
Question: 1 Section 8
Introductory Info 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 -
Litware, Inc. owns and operates 300 convenience stores across the US. The company sells a variety of packaged foods and drinks, as well as a variety of prepared
foods, such as sandwiches and pizzas.
Litware has a loyalty club whereby members can get daily discounts on specific items by providing their membership number at checkout.
Litware employs business analysts who prefer to analyze data by using Microsoft Power BI, and data scientists who prefer analyzing data in Azure Databricks
notebooks.
Requirements -
Business Goals -
Litware wants to create a new analytics environment in Azure to meet the following requirements:
See inventory levels across the stores. Data must be updated as close to real time as possible.
Execute ad hoc analytical queries on historical data to identify whether the loyalty club discounts increase sales of the discounted products.
Every four hours, notify store employees about how many prepared food items to produce based on historical demand from the sales data.
Technical Requirements -
$13$10
Litware identifies the following technical requirements:
Minimize the number of different Azure services needed to achieve the business goals.
Use platform as a service (PaaS) offerings whenever possible and avoid having to provision virtual machines that must be managed by Litware.
Ensure that the analytical data store is accessible only to the company's on-premises network and Azure services.
Use Azure Active Directory (Azure AD) authentication whenever possible.
Use the principle of least privilege when designing security.
Stage Inventory data in Azure Data Lake Storage Gen2 before loading the data into the analytical data store. Litware wants to remove transient data from Data
Lake Storage once the data is no longer in use. Files that have a modified date that is older than 14 days must be removed.
Limit the business analysts' access to customer contact information, such as phone numbers, because this type of data is not analytically relevant.
Ensure that you can quickly restore a copy of the analytical data store within one hour in the event of corruption or accidental deletion.
Planned Environment -
Litware plans to implement the following environment:
The application development team will create an Azure event hub to receive real-time sales data, including store number, date, time, product ID, customer loyalty
number, price, and discount amount, from the point of sale (POS) system and output the data to data storage in Azure.
Customer data, including name, contact information, and loyalty number, comes from Salesforce, a SaaS application, and can be imported into Azure once every
eight hours. Row modified dates are not trusted in the source table.
Product data, including product ID, name, and category, comes from Salesforce and can be imported into Azure once every eight hours. Row modified dates are not
trusted in the source table.
Daily inventory data comes from a Microsoft SQL server located on a private network.
Litware currently has 5 TB of historical sales data and 100 GB of customer data. The company expects approximately 100 GB of new data per month for the next
year.
Litware will build a custom application named FoodPrep to provide store employees with the calculation results of how many prepared food items to produce every
four hours.
Litware does not plan to implement Azure ExpressRoute or a VPN between the on-premises network and Azure. Question What should you do to improve high
availability of the real-time data processing solution?
A. Deploy a High Concurrency Databricks cluster.
B. Deploy an Azure Stream Analytics job and use an Azure Automation runbook to check the status of the job and to start the job if it stops.
C. Set Data Lake Storage to use geo-redundant storage (GRS).
D. Deploy identical Azure Stream Analytics jobs to paired regions in Azure.
Answer: D
Guarantee Stream Analytics job reliability during service updates
Part of being a fully managed service is the capability to introduce new service functionality and improvements at a rapid pace. As a result, Stream Analytics can
have a service update deploy on a weekly (or more frequent) basis. No matter how much testing is done there is still a risk that an existing, running job may break
due to the introduction of a bug. If you are running mission critical jobs, these risks need to be avoided. You can reduce this risk by following Azures paired region
model.
Scenario: The application development team will create an Azure event hub to receive real-time sales data, including store number, date, time, product ID, customer
loyalty number, price, and discount amount, from the point of sale (POS) system and output the data to data storage in Azure
Reference:
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-job-reliability
Monitor and optimize data storage and data processing
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