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NEW QUESTION: 1
Which two advanced WLAN options are required when deploying central web authentication with Cisco ISE? (Choose two.)
A. NAC State SNMP NAC.
B. NAC State RADIUS NAC
C. P2P Blocking Action set to Drop.
D. DHCP Addr. Assignment disabled.
E. Allow AAA override enabled.
Answer: B,E
Explanation:
The WLC configuration is fairly straightforward. Atrickis used (same as on switches) in order to obtain the dynamic authentication URL from the ISE (since it uses Change of Authorization (CoA), a session must be created and the session ID is part of the URL). The SSID is configured in order to use MAC filtering. The ISE is configured in order to return an access-accept even if the MAC address is not found, so that it sends the redirection URL for all users.
In addition to this, RADIUS Network Admission Control (NAC) and Authentication, Authorization, and Accounting (AAA) Override must be enabled. The RADIUS NAC allows the ISE to send a CoA request that indicates that the user is now authenticated and is able to access the network. It is also used for posture assessment, in which case the ISE changes the user profile based on the posture result.
Ensure that the RADIUS server has RFC3576 (CoA) enabled, which is by default.
Reference: http://www.cisco.com/c/en/us/support/docs/security/identity-services-engine/115732-central-web-auth-00.html

NEW QUESTION: 2

A. Option B
B. Option A
C. Option C
D. Option D
Answer: B,C

NEW QUESTION: 3
Which of the following are valid methods for selecting an appropriate model from the model space for severity estimation:
I. Cross-validation method
II. Bootstrap method
III. Complexity penalty method
IV. Maximum likelihood estimation method
A. I and IV
B. II and III
C. All of the above
D. I, II and III
Answer: C
Explanation:
Explanation
Once we have a number of distributions in the model space, the task is to select the "best" distribution that is likely to be a good estimate of true severity. We have a number of distributions to pick from, an empirical dataset (from internal or external losses), and we can estimate the parameters for the different distributions.
We then have to decide which distribution to pick, and that generally requires considering both approximation and fitting errors.
There are three methods that are generally used for selecting a model:
1. The cross-validation method: This method divides the available data into two parts - the training set, and the validation set (the validation set is also called the 'testing set'). Parameter estimation for each distribution is done using the training set, and differences are then calculated based on the validation set. Though the temptation may be to use the entire data set to estimate the parameters, that is likely to result in what may appear to be an excellent fit to the data on which it is based, but without any validation. So we estimate the parameters based on one part of the data (the training set), and check the differences we get from the remaining data (the validation set).
2. Complexity penalty method: This is similar to the cross-validation method, but with an additional consideration of the complexity of the model. This is because more complex models are likely to produce a more exact fit than simpler models, this may be a spurious thing - and therefore a 'penalty' is added to the more complex models as to favor simplicity over complexity. The 'complexity' of a model may be measured by the number of parameters it has, for example, a log-normal distribution has only two parameters while a body-tail distribution combining two different distributions may have many more.
3. The bootstrap method: The bootstrap method estimates fitting error by drawing samples from the empirical loss dataset, or the fit already obtained, and then estimating parameters for each draw which are compared using some statistical technique. If the samples are drawn from the loss dataset, the technique is called a non-parametric bootstrap, and if the sample is drawn from an estimated model distribution, it is called a parametric bootstrap.
4. Using goodness of fit statistics: The candidate fits can be compared using MLE based on the KS distance, for example, and the best one selected. Maximum likelihood estimation is a technique that attempts to maximize the likelihood of the estimate to be as close to the true value of the parameter. It is a general purpose statistical technique that can be used for parameter estimation technique, as well as for deciding which distribution to use from the model space.
All the choices listed are the correct answer.

NEW QUESTION: 4
You need to support the response capability of the E-Commerce Web Application.
Which two technologies should you use? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
A. Azure Bot Service
B. Azure Cognitive Services
C. Azure Security Center
D. Azure Information Protection
E. Azure Batch AI
Answer: A,B
Explanation:
Explanation
Azure Cognitive Services allows your apps to process natural language with pre-built scripts, evaluate sentiment and learn how to recognize what users want.
Your users talk in many places, your bot should too. Azure Bot Service can be integrated across multiple channels to increase interactions and reach more customers using your website or app Scenario: The E-Commerce Web Application:
Must support an interactive response capability to include natural language processing (NLP).
References:
https://azure.microsoft.com/en-us/services/cognitive-services/