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NEW QUESTION: 1
A. Option D
B. Option C
C. Option A
D. Option B
Answer: D
NEW QUESTION: 2
Server2の計画された変更を実装するには、環境を準備する必要があります。
あなたは何をするべきか？回答するには、回答エリアで適切なオプションを選択します。
注：各正しい選択には1ポイントの価値があります。
Answer:
Explanation:
Explanation
Box 1: Create a Recovery Services vault
Create a Recovery Services vault on the Azure Portal.
Box 2: Install the Azure Site Recovery Provider
Azure Site Recovery can be used to manage migration of on-premises machines to Azure.
Scenario: Migrate the virtual machines hosted on Server1 and Server2 to Azure.
Server2 has the Hyper-V host role.
References:
https://docs.microsoft.com/en-us/azure/site-recovery/migrate-tutorial-on-premises-azure
NEW QUESTION: 3
How can you get a neural network to learn about relationships between categories in a categorical feature?
A. Create an embedding column
B. Create a multi-hot column
C. Create a one-hot column
D. Create a hash bucket
Answer: A
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