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International Remuneration - An Overview of Global Rewards

  • Exam Number/Code : GR7
  • Exam Name : International Remuneration - An Overview of Global Rewards
  • Questions and Answers : 213 Q&As
  • Update Time: 2019-01-10
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NEW QUESTION: 1
A network technician configures a firewall's ACL to allow outgoing traffic for several popular services such as email and web browsing. However, after the firewall's deployment, users are still unable to retrieve their emails. Which of the following would BEST resolve this issue?
A. Allow the firewall to accept inbound traffic to ports 21,53,69, and 123.
B. Set the firewall to operate in transparent mode.
C. Allow the firewall to accept inbound traffic to ports 25,67,179 and 3389.
D. Allow the firewall to accept inbound traffic to ports 80,110,143, and 443.
Answer: C

NEW QUESTION: 2
You create an experiment in Azure Machine Learning Studio. You add a training dataset that contains 10,000 rows. The first 9,000 rows represent class 0 (90 percent).
The remaining 1,000 rows represent class 1 (10 percent).
The training set is imbalances between two classes. You must increase the number of training examples for class 1 to 4,000 by using 5 data rows. You add the Synthetic Minority Oversampling Technique (SMOTE) module to the experiment.
You need to configure the module.
Which values should you use? To answer, select the appropriate options in the dialog box in the answer area.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

Explanation

Box 1: 300
You type 300 (%), the module triples the percentage of minority cases (3000) compared to the original dataset (1000).
Box 2: 5
We should use 5 data rows.
Use the Number of nearest neighbors option to determine the size of the feature space that the SMOTE algorithm uses when in building new cases. A nearest neighbor is a row of data (a case) that is very similar to some target case. The distance between any two cases is measured by combining the weighted vectors of all features.
By increasing the number of nearest neighbors, you get features from more cases.
By keeping the number of nearest neighbors low, you use features that are more like those in the original sample.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/smote

NEW QUESTION: 3
프로젝트 중반에 프로젝트 관리자는 프로젝트가 일정보다 훨씬 뒤쳐지고 있다고 판단했습니다. 프로젝트 스폰서는 완료 기한을 맞추기 위해 프로젝트를 진행하기 위해 5 가지 리소스를 공개하기로 동의했습니다.
이것은 다음의 예입니다.
A. 빠른 추적
B. 리소스 레벨링
C. 자원 달력 조정
D. 충돌
Answer: D