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NEW QUESTION: 1
Which two resources and tools are available within the Cisco Meraki Partner Portal? (Choose two)
A. backup login page for the Cisco Meraki dashboard
B. API developer files and virtual sandboxes for proof of concept testing
C. Cisco Meraki Support ticket/case management and live help
D. lead generation tools including custom referral links and templates to webinars
E. registration for Cisco Meraki training series and previous webinar training recordings
Answer: D,E

NEW QUESTION: 2
How can you get a neural network to learn about relationships between categories in a categorical feature?
A. Create a hash bucket
B. Create a multi-hot column
C. Create a one-hot column
D. Create an embedding column
Answer: D
Explanation:
There are two problems with one-hot encoding. First, it has high dimensionality, meaning that instead of having just one value, like a continuous feature, it has many values, or dimensions. This makes computation more time-consuming, especially if a feature has a very large number of categories. The second problem is that it doesn't encode any relationships between the categories. They are completely independent from each other, so the network has no way of knowing which ones are similar to each other.
Both of these problems can be solved by representing a categorical feature with an embedding column.
The idea is that each category has a smaller vector with, let's say, 5 values in it. But unlike a one-hot vector, the values are not usually 0. The values are weights, similar to the weights that are used for basic features in a neural network. The difference is that each category has a set of weights (5 of them in this case).
You can think of each value in the embedding vector as a feature of the category. So, if two categories are very similar to each other, then their embedding vectors should be very similar too. Reference: https:// cloudacademy.com/google/introduction-to-google-cloud-machine-learning-engine-course/a-wide-and- deep-model.html

NEW QUESTION: 3
Intelligent Semiconductor is considering issuing additional common stock. The current yield to maturity on the firm's outstanding senior long-term debt is 13%. The company's combined federal/state income tax is
35%. The risk-free rate of return is 5.6%, and the annual return on the broadest market index is expected to be 13.5%. Shares of Intelligent Semiconductor have a historical beta of 1.6. In the past, the firm has assumed a 265 basis point risk premium when calculating the cost of equity. What is the cost of equity for this proposed common stock issue using the Bond-Yield-plus-Risk-Premium approach?
A. 16.15%
B. 15.65%
C. 18.24%
D. The cost of equity using the Bond-Yield-plus-Risk-Premium approach cannot be calculated from the information provided.
E. 11.20%
Answer: B
Explanation:
Explanation/Reference:
Explanation:
The cost of issuing common stock can be calculated using several methods, including the Bond-Yield- Plus-Risk-Premium approach, Discounted Cash Flow method, or by using the Capital Asset Pricing Model.
In this example, you have been asked to calculate the cost of equity using the Bond-Yield-plus- Risk- Premium approach. This method is a rather ad hoc procedure used by finance professionals, and involves adding a subjective "risk premium" to the yield to maturity of the firm's outstanding debt. This approach should be used with a degree of caution, as the subjective nature of the procedure leaves significant room for variation in estimates. In using the Bond-Yield-Plus-Risk-Premium approach, a subjective risk premium is added to the yield to maturity of the firm's outstanding long-term debt, and the calculation of the correct answer in this case is as follows: {yield to maturity of the outstanding senior debt 13% + subjective risk premium 2.65%}=15.65%.

NEW QUESTION: 4
NetProfiler provides a view of what is occurring on a network at what level of detail?
A. Packet-level detail with one-minute level details.
B. Flow-level detail with one-minute level details.
C. Flow-level details with nanosecond-level details.
D. Packet-level detail with nanosecond-level details.
E. Packet-level detail with microsecond-level details.
Answer: B