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NEW QUESTION: 1
A. O E2
B. R E2
C. IA
D. i L2
E. D EX
Answer: A,E
NEW QUESTION: 2
Your network contains an Active Directory forest. The functional level of the forest is Windows Server 2008
R2.
Your company's corporate security policy states that the password for each user account must be changed
at least every 45 days.
You have a user account named Service1. Service1 is used by a network application named Application1. Every 45 days, Application1 fails.
After resetting the password for Service1, Application1 runs properly.
You need to resolve the issue that causes Application1 to fail. The solution must adhere to the corporate
security policy.
What should you do?
A. Create a new password policy.
B. Run the Set-ADAccountControl cmdlet.
C. Create a new Password Settings object (PSO).
D. Run the Set-ADServiceAccount cmdlet.
Answer: D
NEW QUESTION: 3
ソリューションアーキテクトは、ロードバランサーの背後にあるAmazon EC2インスタンスで実行されるWebアプリケーションを設計しています。転送中のすべてのデータは暗号化する必要があります。
暗号化の要件を満たすソリューションはどれですか？ （2つ選択します。）
A. Application Load Balancer（ALB）とHTTPSリスナーを使用し、ALBおよびEC2インスタンスにSSL証明書をインストールします。
B. TCPリスナーでネットワークロードバランサー（NLB）を使用し、EC2インスタンスでSSLを終了します。
C. HTTPSリスナーでネットワークロードバランサー（NLB）を使用してから、NLBおよびEC2インスタンスにSSL証明書をインストールします。
D. パススルーモードでApplication Load Balancer（ALB）を使用してから、EC2インスタンスでSSLを終了します。
E. TCPリスナーでApplication Load Balancer（ALB）を使用し、EC2インスタンスでSSLを終了します。
Answer: A,B
Explanation:
Explanation
https://docs.aws.amazon.com/elasticloadbalancing/latest/network/create-tls-listener.html
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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