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NEW QUESTION: 1
次の展示に示すアプリケーションアーキテクチャがあります。
ドロップダウンメニューを使用して、グラフィックに表示されている情報に基づいて各ステートメントを完成させる選択を選択します。
注：それぞれ正しい選択は1ポイントの価値があります。
Answer:
Explanation:
Explanation:
References:
https://docs.microsoft.com/en-us/azure/traffic-manager/traffic-manager-routing-methods
https://docs.microsoft.com/en-us/azure/traffic-manager/traffic-manager-monitoring
NEW QUESTION: 2
In an SD-Access solution what is the role of a fabric edge node?
A. to connect external Layer 3- network to the SD-Access fabric
B. to connect the fusion router to the SD-Access fabric
C. to advertise fabric IP address space to external network
D. to connect wired endpoint to the SD-Access fabric
Answer: D
Explanation:
+ Fabric edge node: This fabric device (for example, access or distribution layer
device) connects wired endpoints to the SDA fabric.
NEW QUESTION: 3
Azure Machine Learningワークスペースには、という名前のデータセットがあります。データセット内のデータのサンプルは次のとおりです。
自動機械学習を使用して、価格列を予測するための最適な回帰モデルを見つけたいと考えています。
Azure Machine Learning SDKを使用して、自動機械学習実験を構成する必要があります。
コードをどのように完成させる必要がありますか？回答するには、回答領域で適切なオプションを選択します。
注：正しい選択はそれぞれ1ポイントの価値があります。
Answer:
Explanation:
Explanation
Box 1: training_data
The training data to be used within the experiment. It should contain both training features and a label column (optionally a sample weights column). If training_data is specified, then the label_column_name parameter must also be specified.
Box 2: validation_data
Provide validation data: In this case, you can either start with a single data file and split it into training and validation sets or you can provide a separate data file for the validation set. Either way, the validation_data parameter in your AutoMLConfig object assigns which data to use as your validation set.
Example, the following code example explicitly defines which portion of the provided data in dataset to use for training and validation.
dataset = Dataset.Tabular.from_delimited_files(data)
training_data, validation_data = dataset.random_split(percentage=0.8, seed=1) automl_config = AutoMLConfig(compute_target = aml_remote_compute, task = 'classification', primary_metric = 'AUC_weighted', training_data = training_data, validation_data = validation_data, label_column_name = 'Class' ) Box 3: label_column_name label_column_name:
The name of the label column. If the input data is from a pandas.DataFrame which doesn't have column names, column indices can be used instead, expressed as integers.
This parameter is applicable to training_data and validation_data parameters.
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-train-automl-client/azureml.train.automl.automlconfig.auto
NEW QUESTION: 4
What happens when you attempt to compile and run the following code? Choose all possible answers.
#include <iostream>
using namespace std;
class C {
public:
int _c;
C():_c(0){}
C(int c) { _c = c;}
C operator+=(C & b) {
C tmp; tmp._c = _c+b._c;
return tmp;
} };
ostream & operator<<(ostream & c, const C & v) {
c<<v._c; return c; }
template <class T>
class A {
T_v;
public:
A() {}
A(T v): _v(v){}
T getV() { return _v; }
void add(T & a) { _v+=a; }
};
int main()
{
A<int> b(2);
A<C>a (5);
a.add(C());
cout << a.getV() <<endl;
return 0;
}
A. program will not compile
B. program will display:5
C. program will compile
D. program will cause runtime exception
Answer: B,C
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