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NEW QUESTION: 1
When using CloudHub with the Shared Load Balancer, what is managed EXCLUSIVELY by the API implementation (the Mule application) and NOT by Anypoint Platform?
A. The number of DNS entries allocated to the API implementation
B. The logging configuration that enables log entries to be visible in Runtime Manager
C. The SSL certificates used by the API implementation to expose HTTPS endpoints
D. The assignment of each HTTP request to a particular CloudHub worker
Answer: D

NEW QUESTION: 2
Your company runs Windows Server Update Services (WSUS) on a server named Server1. Server1 runs
Windows Server 2008 R2. Server1 is located on the company intranet.
You configure the WSUS Web site to use SSL.
You need to configure a Group Policy object (GPO) to specify the intranet update locations.
Which URLs should you use?
A. http: //SERVER1
B. http: //SERVER1:8080
C. https: //SERVER1
D. https: //SERVER1:8080
Answer: C

NEW QUESTION: 3
Which of these are examples of a value in a sparse vector? (Select 2 answers.)
A. [0, 0, 0, 1, 0, 0, 1]
B. [0, 1]
C. [0, 5, 0, 0, 0, 0]
D. [1, 0, 0, 0, 0, 0, 0]
Answer: B,D
Explanation:
Categorical features in linear models are typically translated into a sparse vector in which each possible value has a corresponding index or id. For example, if there are only three possible eye colors you can represent 'eye_color' as a length 3 vector: 'brown' would become [1, 0, 0], 'blue' would become [0, 1, 0] and 'green' would become [0, 0, 1]. These vectors are called "sparse" because they may be very long, with many zeros, when the set of possible values is very large (such as all English words).
[0, 0, 0, 1, 0, 0, 1] is not a sparse vector because it has two 1s in it. A sparse vector contains only a single 1.
[0, 5, 0, 0, 0, 0] is not a sparse vector because it has a 5 in it. Sparse vectors only contain
0 s and 1s.
Reference:
https://www.tensorflow.org/tutorials/linear#feature_columns_and_transformations