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NEW QUESTION: 1

A. Option D
B. Option A
C. Option E
D. Option C
E. Option B
Answer: C
Explanation:
Explanation
When you need to update a stack, understanding how your changes will affect running resources before you implement them can help you update stacks with confidence. Change sets allow you to preview how proposed changes to a stack might impact your running resources, for example, whether your changes will delete or replace any critical resources, AWS CloudFormation makes the changes to your stack only when you decide to execute the change set, allowing you to decide whether to proceed with your proposed changes or explore other changes by creating another change set For example, you can use a change set to verify that AWS CloudFormation won't replace your stack's database instances during an update.

NEW QUESTION: 2



A. Option D
B. Option A
C. Option C
D. Option B
Answer: C
Explanation:
An ASP.NET HTTP handler is the process that runs in response to a request that is made to an ASP.NET Web application.
ASP.NET maps HTTP requests to HTTP handlers based on a file name extension.
Incorrect:
Not B: HTTP modules differ from HTTP handlers. An HTTP handler returns a response to a request that is identified by a file name extension or family of file name extensions. In contrast, an HTTP module is invoked for all requests and responses. It subscribes to event notifications in the request pipeline and lets you run code in registered event handlers.
References: https://msdn.microsoft.com/en-us/library/bb398986.aspx

NEW QUESTION: 3
HCM CloudとERP Cloudの両方のマルチピラー実装を実装しています。最初にERPを実装し、次にHCM Cloudを実装します。元帳と勘定体系が正しく定義されていることを確認する必要があります。あなたは何をするべきか？ （3つ選択してください）
A. 値を値セットに割り当てる前に、値セット、COA構造、インスタンスの順に勘定体系を作成します。
B. UCMを使用してファイルベースのスプレッドシートローダーを使用し、勘定体系セグメントの値と階層を大量にロードして維持する
C. 企業構造を作成するときに、迅速な実装スプレッドシートを使用します
D. HCMのEnterprise Structure Configurator（ESC）を最初に使用します
E. 勘定体系を導入します
Answer: C,D,E

NEW QUESTION: 4
How can you get a neural network to learn about relationships between categories in a categorical feature?
A. Create a hash bucket
B. Create a multi-hot column
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

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