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NEW QUESTION: 1
A. Option B
B. Option C
C. Option A
D. Option D
Answer: D
NEW QUESTION: 2
decfloat16タイプの10進浮動小数点数の有効な値は、1E385から
負の数の場合は-1E-383、0、正の数の場合は1E-383〜1E385。
A. 偽
B. 真
Answer: B
NEW QUESTION: 3
class Bird {
public void fly () { System.out.print("Can fly"); }
}
class Penguin extends Bird {
public void fly () { System.out.print("Cannot fly"); }
}
and the code fragment:
class Birdie {
public static void main (String [ ] args) {
fly( ( ) -> new Bird ( ));
fly (Penguin : : new);
}
/* line n1 */
}
A. static void fly (Supplier<? extends Bird> bird) {
LOST
B. static void fly (Consumer<? extends Bird> bird) {
bird.accept( ) fly ();
}
C. static void fly (Supplier<Bird> bird) {
bird.get( ) fly ();
}
D. static void fly (Consumer<Bird> bird) {
bird :: fly (); }
Answer: C
NEW QUESTION: 4
Machine Learning Specialist는 광범위한 경제적 요인을 기반으로 미래 고용률을 예측하는 모델을 구축하고 있습니다. 데이터를 탐색하는 동안 전문가는 입력 기능의 크기가 크게 다르다는 것을 알게 됩니다. 전문가는 더 큰 규모의 변수가 모델을 지배하는 것을 원하지 않습니다.
전문가는 모델 학습을 위해 데이터를 준비하기 위해 무엇을 해야 합니까?
A. 정규화를 적용하여 각 필드의 평균이 0 이고 분산이 1 인지 확인하여 중요한 크기를 제거합니다.
B. 고정 된 크기의 슬라이딩 윈도우를 적용하여 비슷한 크기의 새 기능을 생성하기 위해 OSB (orthogonal sparse bigram) 변환을 적용합니다.
C. 데카르트 곱 변환을 적용하여 크기에 독립적 인 새로운 필드 조합을 만듭니다.
D. 분위수 구간 화를 적용하여 데이터를 범주 형 구간으로 그룹화하여 크기를 분포로 대체하여 데이터의 모든 관계를 유지합니다.
Answer: A
Explanation:
https://docs.aws.amazon.com/machine-learning/latest/dg/data-transformations-reference.html