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NEW QUESTION: 1
A security consultant is hired by a company to determine if an internally developed web application is vulnerable to attacks. The consultant spent two weeks testing the application, and determines that no vulnerabilities are present. Based on the results of the tools and tests available, which of the following statements BEST reflects the security status of the application?
A. The company should deploy a web application firewall to ensure extra security.
B. There are no known vulnerabilities at this time.
C. The company's software lifecycle management improved the security of the application.
D. There are no vulnerabilities in the application.
Answer: B
NEW QUESTION: 2
An organization uses the Previous Employment content type to capture relevant employment details of employees. What do you do to make this content type available in other applications?
A. Make sure it is a non-free-form content type.
B. Make sure it is a free-form content type
C. Specify a Subscriber Code for the content type.
D. Do nothing. After the content type is created, it is available for use in all applications.
Answer: C
Explanation:
Explanation/Reference:
References:
https://docs.oracle.com/cd/E51367_01/talentop_gs/FAITM/F1460273AN1A14A.htm
NEW QUESTION: 3
開発者は、プログラムのコンパイル中に繰り返し発生するコンピューターの問題を再投稿します。コンピュータは最初は動作しますが、コンパイルプロセス中にロックされます。すぐに再起動しても問題は解決しません。次の手順のうち、問題を解決する可能性が最も高いのはどれですか？
A. サーマルペーストを再塗布します
B. メモリモジュールの数を減らします。
C. BIOSファームウェアを更新します。
D. ビデオカードを取り付け直します。
Answer: A
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 an embedding column
C. Create a one-hot column
D. Create a multi-hot column
Answer: B
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