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NEW QUESTION: 1
다음 중 데이터 기밀 유지에 가장 큰 위험을 나타내는 것은 무엇입니까?
A. 사용자에게 관리자 권한이 있습니다
B. 보안 인식 교육이 완료되지 않았습니다
C. 네트워크 이중화가 구현되지 않았습니다
D. 백업 테이프가 암호화되지 않은 상태로 생성됩니다
Answer: D

NEW QUESTION: 2
Computer security should be first and foremost which of the following?
A. Be examined in both monetary and non-monetary terms.
B. Be cost-effective.
C. Be proportionate to the value of IT systems.
D. Cover all identified risks
Answer: B
Explanation:
Explanation/Reference:
Explanation:
Each organization is different in its size, security posture, threat profile, and security budget. One organization may have one individual responsible for information risk management (IRM) or a team that works in a coordinated manner. The overall goal of the team is to ensure the company is protected in the most cost-effective manner.
Incorrect Answers:
A: Not all identified risks are mitigated. Some risks are accepted.
C: It is not true that computer security should be first and foremost examined in both monetary and non- monetary terms.
D: It is not true that computer security should be first and foremost proportionate to the value of IT systems. The value of IT systems does not necessarily mean that more or less security is required.
References:
Harris, Shon, All In One CISSP Exam Guide, 6th Edition, McGraw-Hill, New York, 2013, p. 87

NEW QUESTION: 3
You are creating a new experiment in Azure Machine Learning Studio. You have a small dataset that has missing values in many columns. The data does not require the application of predictors for each column. You plan to use the Clean Missing Data module to handle the missing data.
You need to select a data cleaning method.
Which method should you use?
A. Replace using; Probabilistic PCA
B. Synthetic Minority Oversampling Technique (SMOTE)
C. Replace using MICE
D. Normalization
Answer: A
Explanation:
Replace using Probabilistic PCA: Compared to other options, such as Multiple Imputation using Chained Equations (MICE), this option has the advantage of not requiring the application of predictors for each column. Instead, it approximates the covariance for the full dataset. Therefore, it might offer better performance for datasets that have missing values in many columns.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/clean-missing-data