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NEW QUESTION: 1
Which of these are examples of a value in a sparse vector? (Select 2 answers.)
A. [0, 1]
B. [0, 0, 0, 1, 0, 0, 1]
C. [0, 5, 0, 0, 0, 0]
D. [1, 0, 0, 0, 0, 0, 0]
Answer: A,D
Explanation:
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
NEW QUESTION: 2
When a bathtub curve is used, one characteristic of the flat center portion of the curve is that during this period Response:
A. The reliability of the system can be expressed as an exponential function
B. There can be only one type of failure in the system.
C. The equipment can be considered highly reliable.
D. The time of the next failure is predictable from the time of the last failure.
Answer: A
NEW QUESTION: 3
技術者が顧客に対して肯定的な「いいえ」を使用する必要があるのはいつですか？ （3つ選択してください。）
A. 顧客はサービスオプションについて誤った情報を受け取ります。
B. デバイスは保証対象外です。
C. Appleは新製品を発表しました。
D. 技術者は顧客を支援する気はありません。
E. デバイスは廃止されたデバイスであり、サービスを終了しています。
F. デバイスは修復の対象です。
G. 顧客は新しいデバイスを購入したいと考えています。
Answer: A,B,E
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