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NEW QUESTION: 1
Your Azure Machine Learning workspace has a dataset named real_estate_data. 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
インスタンスがCloudWatchに対してのみ基本的なモニタリングを有効にしました。基本的な監視に使用できる最小期間はどれくらいですか？
A. 300秒
B. 60秒
C. 240秒
D. 360秒
Answer: A
Explanation:
説明
ユーザーがEC2インスタンスメトリックにアラームを設定する場合、期間はメトリック頻度以上にする必要があります。基本的な監視では、メトリックは5分（300秒）ごとに監視されます。
参照：

NEW QUESTION: 3
次の要件のうち、情報セキュリティの優先度が最も低いのはどれですか？
A. プライバシー
B. ビジネス
C. 規制
D. テクニカル
Answer: D
Explanation:
説明
情報セキュリティの優先順位は、技術仕様を上書きする場合があります。その後、技術仕様を書き換えて、最小セキュリティ標準に準拠する必要があります。規制およびプライバシーの要件は政府によって義務付けられているため、オーバーライドの対象にはなりません。情報セキュリティの優先順位を決定する際には、ビジネスのニーズを常に優先する必要があります。

NEW QUESTION: 4

A. Option D
B. Option B
C. Option A
D. Option C
Answer: C
Explanation:
About delivery dates [AX 2012]
The delivery date is determined by several factors. The earliest delivery date is based on
the sales lead time plus the transport time. Working hours are also considered. The sales
lead time is the time that is spent between creating the sales order and shipping the items.
After you set up all the relevant parameters, you can select from a list of available ship
dates and receipt dates.