You need to design a data retention solution for the Twitter feed data records. The solution must meet the customer sentiment analytics requirements.

Which Azure Storage functionality should you include in the solution?

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Topic 2, Litware, inc.

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 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.

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.

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Correct Answer:

Explanation:

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:

✑ 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.

✑ 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.

What should you do to improve high availability of the real-time data processing solution?

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What should you recommend to prevent users outside the Litware on-premises network from accessing the analytical data store?

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What should you recommend using to secure sensitive customer contact information?

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Topic 3, Mix Questions

You have an Azure Data Lake Storage account that has a virtual network service endpoint configured.

You plan to use Azure Data Factory to extract data from the Data Lake Storage account. The data will then be loaded to a data warehouse in Azure Synapse Analytics by using PolyBase.

Which authentication method should you use to access Data Lake Storage?

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HOTSPOT

You have an Azure subscription that contains the following resources:

✑ An Azure Active Directory (Azure AD) tenant that contains a security group named Group1

✑ An Azure Synapse Analytics SQL pool named Pool1

You need to control the access of Group1 to specific columns and rows in a table in Pool1.

Which Transact-SQL commands should you use? To answer, select the appropriate options in the answer area.

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Correct Answer:

Explanation:

Box 1: GRANT

You can implement column-level security with the GRANT T-SQL statement.

Box 2: CREATE SECURITY POLICY

Implement Row Level Security by using the CREATE SECURITY POLICY Transact-SQL statement

Reference: https://docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/column-level-security

HOTSPOT

You need to implement an Azure Databricks cluster that automatically connects to Azure Data Lake Storage Gen2 by using Azure Active Directory (Azure AD) integration.

How should you configure the new cluster? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

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Correct Answer:

Explanation:

Box 1: High Concurrency

Enable Azure Data Lake Storage credential passthrough for a high-concurrency cluster.

Incorrect:

Support for Azure Data Lake Storage credential passthrough on standard clusters is in Public Preview.

Standard clusters with credential passthrough are supported on Databricks Runtime 5.5 and above and are limited to a single user.

Box 2: Azure Data Lake Storage Gen1 Credential Passthrough

You can authenticate automatically to Azure Data Lake Storage Gen1 and Azure Data Lake Storage Gen2 from Azure Databricks clusters using the same Azure Active Directory (Azure AD) identity that you use to log into Azure Databricks. When you enable your cluster for Azure Data Lake Storage credential passthrough, commands that you run on that cluster can read and write data in Azure Data Lake Storage without requiring you to configure service principal credentials for access to storage.

Reference: https://docs.azuredatabricks.net/spark/latest/data-sources/azure/adls-passthrough.html

You have an Azure Synapse Analystics dedicated SQL pool that contains a table named Contacts.

Contacts contains a column named Phone.

You need to ensure that users in a specific role only see the last four digits of a phone number when querying the Phone column.

What should you include in the solution?

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HOTSPOT

You have an Azure Synapse Analytics dedicated SQL pool that contains the users shown in the following table.

User1 executes a query on the database, and the query returns the results shown in the following exhibit.

User1 is the only user who has access to the unmasked data.

Use the drop-down menus to select the answer choice that completes each statement based on the information presented in the graphic. NOTE: Each correct selection is worth one point.

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Correct Answer:

Explanation:

Box 1: 0

The YearlyIncome column is of the money data type.

The Default masking function: Full masking according to the data types of the designated fields

✑ Use a zero value for numeric data types (bigint, bit, decimal, int, money, numeric, smallint, smallmoney, tinyint, float, real).

Box 2: the values stored in the database

Users with administrator privileges are always excluded from masking, and see the original data without any mask.

Reference: https://docs.microsoft.com/en-us/azure/azure-sql/database/dynamic-data-masking-overview