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NEW QUESTION: 1
The management of Allcycles.com, a motorcycle supply chain, is examining several capital projects. The firm is financed according to the following schedule based on market values:
60% debt
35% common stock
5% perpetual preferred stock
Additionally, consider the following information:
Yield on outstanding debt: 10.12%
Tax rate: 35%
Annual preferred dividend: $0.64
Preferred stock price: $7.36
Return on equity: 18%
Dividend payout ratio: 25%
Cost of common stock: 16.33%
Using this information, what is the Weighted Average Cost of Capital for Allcycles.com?
A. The answer cannot be completely calculated from the given information.
B. 10.10%
C. None of these answers.
D. 10.04%
E. 9.98%
F. 10.55%
Answer: B
Explanation:
Explanation/Reference:
Explanation:
In order to calculate the WACC, it is necessary to first calculate the component after-tax cost of debt, common equity, and preferred equity. Once the cost of these components is determined, they are imputed into the WACC equation, which is as follows:
{WACC = [(% weight of debt securities * cost of debt) + (% weight of common stock * cost of common stock) + (% weight of preferred stock * cost of preferred stock)]} To calculate the component after-tax cost of debt, use the following equation:
{Cost of debt = [yield on outstanding debt securities * (1 - tax rate)} Factoring in the given information into this equation would yield the following:
{After-tax cost of debt = [10.12% * (1 - 0.35%)]} = 6.578%
To calculate the component cost of outstanding preferred stock, the following equation must be used:
{Cost of preferred stock = [annual dividend / preferred stock price]}
{Cost of preferred stock - = [$0.64 / $7.36]} = 8.70%.
The final component of the WACC calculation, the cost of common equity, has been provided as 16.33%.
Now that the after-tax cost of debt, preferred stock, and common stock have been determined, the WACC calculation can be found. The calculation of the WACC is as follows:
{[0.60 * 0.06578] + [0.35 * 0.1633] + [0.05 * 0.0870]} = 10.10%.
NEW QUESTION: 2
A bond with a face value of Rs. 1000, returns Rs. 80 in year 1, Rs. 80 in year 2 and Rs. 1080 in year 3. What is the duration of the bond in years?
A. 2.2
B. 2.8
C. 2.6
D. 2.4
Answer: B
NEW QUESTION: 3
Overview:
Litware, Inc. is a company that manufactures personal devices to track physical activity and other health- related data.
Litware has a health tracking application that sends health-related data from a user's personal device to Microsoft Azure.
Litware has three development and commercial offices. The offices are located in the United States, Luxembourg, and India.
Litware products are sold worldwide. Litware has commercial representatives in more than 80 countries.
Existing Environment:
In addition to using desktop computers in all of the offices, Litware recently started using Microsoft Azure resources and services for both development and operations.
Litware has an Azure Machine Learning solution.
Litware recently extended its platform to provide third-party companies with the ability to upload data from devices to Azure. The data can be aggregated across multiple devices to provide users with a comprehensive view of their global health activity.
While the upload from each device is small, potentially more than 100 million devices will upload data daily by using an Azure event hub.
Each health activity has a small amount of data, such as activity type, start date/time, and end date/time.
Each activity is limited to a total of 3 KB and includes a customer identification key.
In addition to the Litware health tracking application, the users' activities can be reported to Azure by using an open API.
The developers at Litware perform Machine Learning experiments to recommend an appropriate health activity based on the past three activities of a user.
The Litware developers train a model to recommend the best activity for a user based on the hour of the day.
Requirements:
Litware plans to extend the existing dashboard features so that health activities can be compared between the users based on age, gender, and geographic region.
Minimize the costs associated with transferring data from the event hub to Azure Storage.
Litware identifies the following technical requirements:
Data from the devices must be stored for three years in a format that enables the fast processing of
date fields and filtering.
The third-party companies must be able to use the Litware Machine Learning models to generate
recommendations to their users by using a third-party application.
Any changes to the health tracking application must ensure that the Litware developers can run the
experiments without interrupting or degrading the performance of the production environment.
Activity tracking data must be available to all of the Litware developers for experimentation. The developers must be prevented from accessing the private information of the users.
When the Litware health tracking application asks users how they feel, their responses must be reported to Azure.
You need to recommend a data handling solution to support the planned changes to the dashboard.
What is the best recommendation to achieve the goal? More than one answer choice may achieve the goal. Select the BEST answer.
A. anonymization
B. obfuscation
C. encryption
D. compression
Answer: B
Explanation:
Explanation/Reference:
Explanation:
From scenario: Litware plans to extend the existing dashboard features so that health activities can be compared between the users based on age, gender, and geographic region.
The developers must be prevented from accessing the private information of the users.
Dynamic Data Masking can be used to hide or obfuscate sensitive data, by controlling how the data appears in the output of database queries.
Dynamic Data Masking rules can be defined on particular columns, indicating how the data in those columns will appear when queried. There are no physical changes to the data in the database itself; the data remains intact and is fully available to authorized users or applications. Database operations remain unaffected, and the masked data has the same data type as the original data, so DDM can often be applied without making any changes to database procedures or application code.
Reference: https://blogs.technet.microsoft.com/dataplatforminsider/2016/01/25/use-dynamic-data- masking-to-obfuscate-your-sensitive-data/
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