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NEW QUESTION: 1
Given:
1 . class Test {
2 . public static void main(String args[]) {
3 . String str = new String("Welcome");
4 . // insert code here
5 . }
6 . }
Which, inserted at line 4, produces the output true?
A. System.out.println(str.startsWith('W'));
B. System.out.println(str.indexOf("W"));
C. System.out.println(str.trim());
D. System.out.println(str.startsWith("W"));
E. System.out.println(str.indexOf(1));
Answer: D

NEW QUESTION: 2
Exchange Server 2016組織があります。 組織には、Exchange Server 2010またはExchange Server 2016のいずれかがインストールされているサーバーが含まれています。
EX01という名前のExchange Server 2010サーバーからEX02という名前のExchange Server 2016サーバーに数千のメールボックスを移行しています。 EX01には、単一のメールボックスデータベースが含まれています。
20個のメールボックスのみが同時に移動することがわかります。
同時に移動するメールボックスの数を増やす必要があります
あなたは何をするべきか？
A. Run the Set-MoveRequest cmdlel
B. Edit the MsExchangeMailboxReplicatioan.exe.config file
C. Run the Set-MigrationBatch cmdlet
D. Edit the MSExchangeMigrationWorkflow.exe.config file.
Answer: B

NEW QUESTION: 3
You want Oracle Inventory to automatically create move orders while performing min-max planning. To enable that functionality, you would need to define the ____.
A. subinventory supply source type at the pull level
B. Move Order Timeout Period to a value greater than zero
C. subinventory supply source type at the subinventory or item level
D. Move Order Timeout Action to a value greater than zero
Answer: C

NEW QUESTION: 4
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 hash bucket
C. Create a one-hot column
D. Create a multi-hot column
Answer: A
Explanation:
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-dee