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NEW QUESTION: 1
Which three features require Cisco Express Forwarding? (Choose three.)
A. UplinkFast
B. BackboneFast
C. fragmentation
D. AutoQoS
E. MPLS
F. NBAR
Answer: D,E,F

NEW QUESTION: 2
Click the Exhibit button.
Which two are true? (Choose two.)

A. foo is public
B. foo is private
C. foo is an operation
D. foo is an attribute
Answer: A,D

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, 5, 0, 0, 0, 0]
C. [0, 1]
D. [1, 0, 0, 0, 0, 0, 0]
Answer: C,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 0s and 1s.
Reference: https://www.tensorflow.org/tutorials/linear#feature_columns_and_transformations