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NEW QUESTION: 1
A. Option A
B. Option C
C. Option F
D. Option E
E. Option B
F. Option D
Answer: D
Explanation:
The Maximum Transmission Unit (MTU) defines the maximum Layer 3 packet (in bytes) that the layer can pass onwards.
NEW QUESTION: 2
An AWS account owner has setup multiple IAM users. One IAM user only has CloudWatch access. He has setup the alarm action which stops the EC2 instances when the CPU utilization is below the threshold limit. What will happen in this case?
A. CloudWatch will stop the instance when the action is executed
B. It is not possible to stop the instance using the CloudWatch alarm
C. The user cannot set an alarm on EC2 since he does not have the permission
D. The user can setup the action but it will not be executed if the user does not have EC2 rights
Answer: D
Explanation:
Amazon CloudWatch alarms watch a single metric over a time period that the user specifies and performs one or more actions based on the value of the metric relative to a given threshold over a number of time periods. The user can setup an action which stops the instances when their CPU utilization is below a certain threshold for a certain period of time. The EC2 action can either terminate or stop the instance as part of the EC2 action. If the IAM user has read/write permissions for Amazon CloudWatch but not for Amazon EC2, he can still create an alarm. However, the stop or terminate actions will not be performed on the Amazon EC2 instance.
NEW QUESTION: 3
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:
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