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NEW QUESTION: 1
You are using Pega Express. You want to avoid creating unnecessary case level properties and views.
Which three actions do you take to accomplish this goal? (Choose Three.)
A. Create a data type that corresponds to each case type.
B. Pre-define properties, sections, and relevant records that are applied to work Cover
C. Add an embedded Page property to the case type.
D. Define case type view using ''. Page.property ''syntax for the majority of the filed.
E. Drag-and-drop a Filed Group when defining a case view.
Answer: B,C,D

NEW QUESTION: 2
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: 3
An errant process is known to use an entire processor and run at 100%. A SysOps Administrator wants to automate restarting the instance once the problem occurs for more than 2 minutes.
How can this be accomplished?
A. Create a Lambda function to restart the EC2 instance, triggered by EC2 health checks.
B. Create a CloudWatch alarm for the EC2 instance with detailed monitoring. Enable an action to restart the instance.
C. Create an Amazon CloudWatch alarm for the EC2 instance with basic monitoring. Enable an action to restart the instance.
D. Create an AWS Lambda function to restart the EC2 instance, triggered on a scheduled basis every 2 minutes.
Answer: B