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NEW QUESTION: 1
Your Azure Machine Learning workspace has a dataset named real_estate_dat a. A sample of the data in the dataset follows.

You want to use automated machine learning to find the best regression model for predicting the price column.
You need to configure an automated machine learning experiment using the Azure Machine Learning SDK.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

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.
Incorrect Answers:
X: The training features to use when fitting pipelines during an experiment. This setting is being deprecated. Please use training_data and label_column_name instead.
Y: The training labels to use when fitting pipelines during an experiment. This is the value your model will predict. This setting is being deprecated. Please use training_data and label_column_name instead.
X_valid: Validation features to use when fitting pipelines during an experiment.
If specified, then y_valid or sample_weight_valid must also be specified.
Y_valid: Validation labels to use when fitting pipelines during an experiment.
Both X_valid and y_valid must be specified together.
exclude_nan_labels: Whether to exclude rows with NaN values in the label. The default is True.
y_max: y_max (float)
Maximum value of y for a regression experiment. The combination of y_min and y_max are used to normalize test set metrics based on the input data range. If not specified, the maximum value is inferred from the data.
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-train-automl-client/azureml.train.automl.automlconfig.automlconfig?view=azure-ml-py

NEW QUESTION: 2
企業は、オンプレミスアプリケーションからAWSサービスへのすべてのアクセスが、パブリックインターネットではなく、AWS Direct Connect接続を経由することを要求します。 SysOps管理者はこの要件をどのように実装しますか？
A. AWSシールドを構成して、AWSマネジメントコンソールがデータセンター範囲内以外のIPアドレスからアクセスされないようにします
B. すべてのVPCネットワークACLを更新して、データセンターのIP範囲からのアクセスを許可します
C. aws：sourceConnection条件を使用してAWS Direct Connect接続IDからのアクセスのみを許可するIAMポリシーを実装する
D. AWS Direct Connect接続でパブリック仮想インターフェイスをセットアップします
Answer: D

NEW QUESTION: 3
展示を参照してください。

FortiSwitchからRADIUSサーバーに送信されたRADIUSアクセス要求パケットを含む展示に示されているパケットキャプチャを調べます。
RADIUSアクセス要求パケットの[ユーザー名]フィールドにMACアドレスが含まれているのはなぜですか。
A. FortiSwitchは、MACアドレスをユーザー名として使用して自身を認証します。
B. 接続されたデバイスがマシン認証を行っています
C. FortiSwitchは、RADIUSサーバーから送信されたアクセスチャレンジパケットに応答し、クライアントのMACアドレスを要求しています。
D. FortiSwitchインターフェイスはMACアドレスバイパスを使用した802 IXポート認証用に構成されており、接続されたデバイスは802.1Xをサポートしていません。
Answer: C

NEW QUESTION: 4
In which of the following case/cases Revocation of Power of Attorney is possible?

A. (i), (ii) and (iii)
B. Only (ii)
C. Only (i)
D. Both (i) and (ii)
Answer: A