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NEW QUESTION: 1
A security analyst receives a SIEM alert that someone logged in to the appadmin test account, which is only used for the early detection of attacks. The security analyst then reviews the following application log:

Which of the following can the security analyst conclude?
A. A replay attack is being conducted against the application.
B. A credentialed vulnerability scanner attack is testing several CVEs against the application.
C. A service account password may have been changed, resulting in continuous failed logins within the application.
D. An injection attack is being conducted against a user authentication system.
Answer: C

NEW QUESTION: 2
How can you get a neural network to learn about relationships between categories in a categorical feature?
A. Create a multi-hot column
B. Create a hash bucket
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
A bank uses a Common Data Service solution to manage clients.
Bank representatives perform client credit checks while the client is present. Credit checks may take up to five minutes to complete.
Bank policy dictates that the bank representative's app must stay blocked until credit checks are complete.
You need to display a model-driven app while credit checks run to ask the bank representative and client to wait for the credit check to complete.
Which function should you use?
A. Xrm.Utility.showProgressIndicator(myPoliteMessage)
B. Xrm.Navigation.openWebResource("prefix.myPoliteMessage.html")
C. Xrm.Utility.openWebResource("prefix_myPoliteMessage.html")
D. Xrm.Navigation.openAlertDialog(myPoliteMessage)
Answer: A
Explanation:
showProgressIndicator displays a progress dialog with the specified message.
Any subsequent call to this method will update the displayed message in the existing progress dialog with the message specified in the latest method call.
The progress dialog blocks the UI until it is closed using the closeProgressIndicator method. So, you must use this method with caution.
Reference:
https://docs.microsoft.com/en-us/powerapps/developer/model-driven-apps/clientapi/reference/xrm-utility/showprogressindicator

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