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NEW QUESTION: 1

A. Option B
B. Option C
C. Option A
D. Option D
Answer: A,D
Explanation:
Reference: http://www.boche.net/blog/index.php/2013/09/13/vsphere-5-5-unmap-deep-dive/

NEW QUESTION: 2
Mit welchen Transaktionen können nach einem System-Upgrade Modifikationsanpassungen durchgeführt werden?
Es gibt 2 richtige Antworten auf diese Frage
A. Spool-Administration: Einstiegsbild (Transaktion SPAD) zum Anpassen von ABAP Repository-Objekten
B. Modifikationsanpassung: Dictionary-Objektauswahl (Transaktion SPDD) zum Anpassen von ABAP Dictionary-Objekten
C. Object Navigator (Transaktion SPAU_ENH) zum Anpassen von ABAP Dictionary-Objekten
D. Modifikationsanpassung: Objektauswahl (Transaktion SPAU) zum Anpassen von ABAP-Repository-Objekten
Answer: B,D

NEW QUESTION: 3
How can you get a neural network to learn about relationships between categories in a categorical feature?
A. Create a multi-hot column
B. Create a hash bucket
C. Create an embedding column
D. Create a one-hot column
Answer: C
Explanation:
There are two problems with one-hot encoding. First, it has high dimensionality, meaning that instead of having just one value, like a continuous feature, it has many values, or dimensions. This makes computation more time-consuming, especially if a feature has a very large number of categories. The second problem is that it doesn't encode any relationships between the categories. They are completely independent from each other, so the network has no way of knowing which ones are similar to each other.
Both of these problems can be solved by representing a categorical feature with an embedding column. The idea is that each category has a smaller vector with, let's say, 5 values in it.
But unlike a one-hot vector, the values are not usually 0. The values are weights, similar to the weights that are used for basic features in a neural network. The difference is that each category has a set of weights (5 of them in this case).
You can think of each value in the embedding vector as a feature of the category. So, if two categories are very similar to each other, then their embedding vectors should be very similar too.
Reference: https://cloudacademy.com/google/introduction-to-google-cloud-machine-learning-engine-course/a-wide-and-deep-model.html

NEW QUESTION: 4
다음 중 공공 클라우드 서비스를 사용하는 조직에 가장 효과적인 미디어 소독 기술은 무엇입니까?
A. 드라이브 소거
B. 보안 등급 덮어 쓰기 삭제
C. 하위 수준 서식 지정
D. 암호화 삭제
Answer: B