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NEW QUESTION: 1
온 프레미스 인프라와 Amazon VPC 간의 연결 솔루션을 설계하고 있습니다. 온 프레미스 서버는 VPC 인스턴스와 통신합니다. 인터넷을 통해 IPSec 터널을 설정합니다. VPN 게이트웨이를 사용하고 AWS 지원 고객 게이트웨이에서 IPSec 터널을 종료합니다.
위에서 설명한 IPSec 터널을 구현하여 다음 중 어떤 목표를 달성 하시겠습니까?
(답변 4 개 선택)
A. 인터넷을 통해 전송되는 데이터 보호
B. 인터넷을 통한 데이터 암호화
C. VPN 게이트웨이와 고객 게이트웨이 간의 피어 ID 인증
D. 인터넷을 통한 데이터 무결성 보호
E. 전송중인 데이터의 종단 간 보호
F. 종단 간 ID 인증
Answer: A,B,C,D
NEW QUESTION: 2
Which design resource allows you and the customer to view examples of collaboration solutions based on scenarios products, or experiences?
A. Quick Pricing tool
B. HCS Configuration tool
C. Project Workplace
D. Virtual Machine Placement tool
Answer: C
Explanation:
Explanation
https://projectworkplace.cisco.com/#/en-us
NEW QUESTION: 3
Which of the following adheres to the guardrail "Do nothing that is hard"? (Choose One)
A. Using standard, out of the box objects and properties as relevant
B. Creating custom HTML screens that robustly cover all possible application use cases
C. Using a application specific harness named Process instead of the standard harness named Perform
D. Modifying the starter flow to avoid using subflows as much as possible
Answer: A
NEW QUESTION: 4
You are evaluating a Python NumPy array that contains six data points defined as follows:
data = [10, 20, 30, 40, 50, 60]
You must generate the following output by using the k-fold algorithm implantation in the Python Scikit-learn machine learning library:
train: [10 40 50 60], test: [20 30]
train: [20 30 40 60], test: [10 50]
train: [10 20 30 50], test: [40 60]
You need to implement a cross-validation to generate the output.
How should you complete the code segment? To answer, select the appropriate code segment in the dialog box in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Box 1: k-fold
Box 2: 3
K-Folds cross-validator provides train/test indices to split data in train/test sets. Split dataset into k consecutive folds (without shuffling by default).
The parameter n_splits ( int, default=3) is the number of folds. Must be at least 2.
Box 3: data
Example: Example:
>>>
>>> from sklearn.model_selection import KFold
>>> X = np.array([[1, 2], [3, 4], [1, 2], [3, 4]])
>>> y = np.array([1, 2, 3, 4])
>>> kf = KFold(n_splits=2)
>>> kf.get_n_splits(X)
2
>>> print(kf)
KFold(n_splits=2, random_state=None, shuffle=False)
>>> for train_index, test_index in kf.split(X):
print("TRAIN:", train_index, "TEST:", test_index)
X_train, X_test = X[train_index], X[test_index]
y_train, y_test = y[train_index], y[test_index]
TRAIN: [2 3] TEST: [0 1]
TRAIN: [0 1] TEST: [2 3]
References:
https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html