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NEW QUESTION: 1
Note: This question is part of a series of questions that use the same scenario. For you convenience, the scenario is repeated in each question. Each question presents a different goal and answer choices, but the text of the scenario is exactly the same in each question in this series.
Start of repeated scenario.
You work for a company named Contoso, Ltd.
The network contains an Active Directory forest named contoso.com. A forest trust exists between contoso.com and an Active Directory forest named adatum.com.
The contoso.com forest contains the objects configured as shown in the following table.

Group1 and Group2 contain only user accounts.
Contoso hires a new remote user named User3. User3 will work from home and will use a computer named Computer3 that runs Windows 10. Computer3 is currently in a workgroup.
An administrator named Admin1 is a member of the Domain Admins group in the contoso.com domain.
From Active Directory Users and Computers, you create an organizational unit (OU) named OU1 in the contoso.com domain, and then you create a contact named Contact1 in OU1.
An administrator of the adatum.com domain runs the Set-ADUser cmdlet to configure a user named User1 to have a user logon name of [email protected].
End of repeated scenario.
You need to ensure that User2 can add Group4 as a member of Group5.
What should you modify?
A. the Managed By settings of Group4
B. the Managed By settings of Group5
C. the group scope of Group4
D. the group scope of Group5
Answer: B

NEW QUESTION: 2

A. Service contracts
B. Operational level agreements (OLAs)
C. Service level agreements (SLAs)
D. Underpinning contracts (UCs)
Answer: A

NEW QUESTION: 3
Windowsデバイスのタッチスクリーン機能が機能しなくなりました。
技術者が最初に確認する必要があるのは次のうちどれですか？
A. デジタイザー設定
B. デバイスマネージャー
C. パフォーマンスモニター
D. システム構成
Answer: A

NEW QUESTION: 4
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

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