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NEW QUESTION: 1
You have just executed a MapReduce job. Where is intermediate data written to after being emitted from
the Mapper's map method?
A. Intermediate data in streamed across the network from Mapper to the Reduce and is never written to
disk.
B. Into in-memory buffers on the TaskTracker node running the Reducer that spill over and are written into
HDFS.
C. Into in-memory buffers on the TaskTracker node running the Mapper that spill over and are written into
HDFS.
D. Into in-memory buffers that spill over to the local file system (outside HDFS) of the TaskTracker node
running the Reducer
E. Into in-memory buffers that spill over to the local file system of the TaskTracker node running the
Mapper.
Answer: E

NEW QUESTION: 2
If a customer has an application that uses a customized protocol, what LTM feature can help optimize the traffic from the application?
A. iRules
B. HTTP classes
C. Packet filtering
D. Transparent virtual servers
E. Networkvirtual servers
Answer: A
Explanation:
Explanation/Reference:
Explanation:

NEW QUESTION: 3
次のように定義された6つのデータポイントを含むPython NumPy配列を評価しています。
データ= [10、20、30、40、50、60]
Python Scikit-learn機械学習ライブラリのk-foldアルゴリズムの埋め込みを使用して、次の出力を生成する必要があります。
train: [10 40 50 60], test: [20 30]
train: [20 30 40 60], test: [10 50]
train: [10 20 30 50], test: [40 60]
出力を生成するには、相互検証を実装する必要があります。
どのようにコードセグメントを完成させるべきですか？回答するには、回答領域のダイアログボックスで適切なコードセグメントを選択します。
注：それぞれの正しい選択には1ポイントの価値があります。

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

NEW QUESTION: 4
How does the Microsoft Project Server calculate slack when you have multiple projects linked together to a master project acting as a program?
A. Microsoft Project Server finds the due date of milestones and calculates backwards based on early start between projects.
B. Microsoft Project Server cannot calculate float or slack between linked projects.
C. Microsoft Project Server only uses project completion dates to determine when the next project may begin.
D. Microsoft Project Server calculates slack by subtracting Early Finish from Early start.
Answer: A
Explanation:
Microsoft Project Server can use the scheduled completion of milestones within projects of a program to
calculate the slack and critical path. When a project is running late, for example, Microsoft Project Server
can predict the effect of a late project and successor projects and activities. Answer option C is incorrect.
This is one part of the formula to find the slack or float for an individual activity. Answer option D is incorrect.
Microsoft Project Server can find float and critical path calculations for a program. Answer option B is
incorrect. Microsoft Project Server can evaluate projects that are linked together in a master project.