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NEW QUESTION: 1
顧客は、高レベルのネットワーク帯域幅と信頼性を必要とします。高可用性と高速スループットを実現するために、スイッチ間に複数の接続を使用して冗長性を提供できる1組のHPEスイッチを設置しています。
顧客が必要とする機能を有効にするには、スイッチにどのテクノロジを設定する必要がありますか。
A. 高速スパニングツリープロトコル（RSTP）
B. マルチシャーシリンクアグリゲーション（MLAG）
C. マルチスパニングツリープロトコル（MSTP）
D. 仮想拡張ローカルエリアネットワーク（VxLAN）
Answer: B

NEW QUESTION: 2
A network administrator needs to configure a serial link between the main office and a remote location. The router at the remote office is a non-Cisco router.
How should the network administrator configure the serial interface of the main office router to make the connection?
A. Main(config)# interface serial 0/0
Main(config-if)#ip address 172.16.1.1 255.255.255.252
Main(config-if)# no shut
B. Main(config)# interface serial 0/0
Main(config-if)#ip address 172.16.1.1 255.255.255.252
Main(config-if)#encapsulation frame-relay
Main(config-if)# authentication chap
Main(config-if)# no shut
C. Main(config)# interface serial 0/0
Main(config-if)#ip address 172.16.1.1 255.255.255.252
Main(config-if)#encapsulation PPP
Main(config-if)# no shut
D. Main(config)# interface serial 0/0
Main(config-if)#ip address 172.16.1.1 255.255.255.252
Main(config-if)#encapsulation ietf
Main(config-if)# no shut
Answer: C

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 one-hot column
C. Create a hash bucket
D. Create an embedding column
Answer: D
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

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