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NEW QUESTION: 1
기계 학습 엔지니어가 Amazon SageMaker Linear Learner 알고리즘을 사용하여 지도 학습 작업을 위한 데이터 프레임을 준비하고 있습니다. ML 엔지니어는 대상 레이블 클래스가 매우 불균형하고 여러 특성 열에 누락 된 값이 있음을 확인합니다. 전체 데이터 프레임에서 누락 된 값의 비율은 5 % 미만입니다.
ML 엔지니어는 누락 된 값으로 인한 편향을 최소화하기 위해 무엇을 해야 합니까?
A. 각 결 측값을 동일한 열의 결 측값이 아닌 값의 평균 또는 중앙값으로 바꿉니다.
B. 각 기능에 대해 다른 기능을 기반으로지도 학습을 사용하여 결 측값을 근사화합니다.
C. 각 결 측값을 동일한 행의 결 측값이 아닌 값의 평균 또는 중앙값으로 바꿉니다.
D. 결 측값이 포함 된 관측치는 데이터의 5 % 미만을 나타내므로 삭제합니다.
Answer: B
Explanation:
Use supervised learning to predict missing values based on the values of other features. Different supervised learning approaches might have different performances, but any properly implemented supervised learning approach should provide the same or better approximation than mean or median approximation, as proposed in responses A and C.
Supervised learning applied to the imputation of missing values is an active field of research.

NEW QUESTION: 2
Which type of attack affects the availability of the target?
A. cross-site scripting
B. denial of service
C. SQL injection
D. social engineering
Answer: B

NEW QUESTION: 3
Which of the following is the BEST way to mitigate the impact of ransomware attacks?
A. Requiring password changes for administrative accounts
B. Paying the ransom
C. Invoking the disaster recovery plan (DRP)
D. Backing up data frequently
Answer: A

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