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NEW QUESTION: 1
プロジェクトのスコープ降格への変更は、クライアントによって口頭で承認されました。ただし、これらの変更は正式に記録されました。
A. スコープを変更するためのフォーマット変更要求を発行するようクライアントに要求します
B. クライアントの承認が保留されている間、チームメンバーに作業を進めるようにアドバイスします。
C. 上級管理職からガイダンスを求める
D. プロジェクト管理計画を更新する
Answer: A

NEW QUESTION: 2
Which of these are examples of a value in a sparse vector? (Select 2 answers.)
A. [0, 1]
B. [0, 5, 0, 0, 0, 0]
C. [0, 0, 0, 1, 0, 0, 1]
D. [1, 0, 0, 0, 0, 0, 0]
Answer: A,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

NEW QUESTION: 3
Which of the following is generally not true in regard to trade secrets?
A. None of the above.
B. Secret information does have to be specifically identifiable.
C. The more a business narrowly defines what it regards as a secret, the easier it is to protect that body of information.
D. It is difficult to protect a trade secret that can be found in publicly accessible sources.
E. Secret information must be effectively protected.
Answer: A