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NEW QUESTION: 1
Complete this statement from the options provided. Using Captive Portal, unidentified users may be either; blocked, allowed to enter required credentials, or required to download the
_____________.
A. Full Endpoint Client
B. SecureClient
C. ICA Certificate
D. Identity Awareness Agent
Answer: D

NEW QUESTION: 2
How is a training data set constructed from user questions for the IBM Watson Natural Language Classifier?
A. Use an XML formatted data set.
B. Group the questions into classes.
C. Upload documents with similar questions.
D. Create an intents index in a spreadsheet.
Answer: B
Explanation:
Explanation/Reference:
Reference: https://www.ibm.com/watson/developercloud/doc/natural-language-classifier/using-your- data.html#constructing-training-data

NEW QUESTION: 3
HOTSPOT
Your network contains an Active Directory domain named contoso.com. The domain contains a domain controller named DC1 and a member server named Server1. All servers run Windows Server 2012 R2.
You install the IP Address Management (IPAM) Server feature on Server1.
From the Provision IPAM wizard, you select the Group Policy Based provisioning method and enter a GPO name prefix of IPAM1.
You need to provision IPAM by using Group Policy.
What command should you run on Server1 to complete the process?
To answer, select the appropriate options in the answer area.
Hot Area:

Answer:
Explanation:

NEW QUESTION: 4
You create an experiment in Azure Machine Learning Studio. You add a training dataset that contains 10,000 rows. The first 9,000 rows represent class 0 (90 percent).
The remaining 1,000 rows represent class 1 (10 percent).
The training set is imbalances between two classes. You must increase the number of training examples for class 1 to 4,000 by using 5 data rows. You add the Synthetic Minority Oversampling Technique (SMOTE) module to the experiment.
You need to configure the module.
Which values should you use? To answer, select the appropriate options in the dialog box in the answer area.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

Explanation:
Box 1: 300
You type 300 (%), the module triples the percentage of minority cases (3000) compared to the original dataset (1000).
Box 2: 5
We should use 5 data rows.
Use the Number of nearest neighbors option to determine the size of the feature space that the SMOTE algorithm uses when in building new cases. A nearest neighbor is a row of data (a case) that is very similar to some target case. The distance between any two cases is measured by combining the weighted vectors of all features.
By increasing the number of nearest neighbors, you get features from more cases.
By keeping the number of nearest neighbors low, you use features that are more like those in the original sample.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/smote