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NEW QUESTION: 1
Azure Machine Learningワークスペースには、real_estate_data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.
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
AWS Lambda関数は3MBのJSONファイルを生成し、それを毎日Amazon S3バケットにアップロードします。 ファイルには機密情報が含まれているため、開発者はバケットにアップロードする前に暗号化されていることを確認する必要があります。
データをバケットにアップロードする前に暗号化されていることを確認するために開発者が行うべき修正はどれですか？
A. S3管理キーを使用してGenerateDataKey APIを呼び出してファイルを暗号化します。
B. Lambda機能コードでS3用に作成されたカスタムKMS顧客マスターキーを使用します。
C. LambdaファンクションコードでS3のデフォルトのAWS KMSカスタマーマスターキーを使用します。
D. GenerateDateKey APIを使用してから、そのデータキーを使用してLambdaファンクションコードでファイルを暗号化します。
Answer: D
NEW QUESTION: 3
John Doe belongs to two business units-"Professional Services Delivery" and "Professional Services Sales"-in OIA. The "globaluserkey" for John Doe in the "globalusers" table is 101. How many entries would you find for "101" in the "bu_globalusers" table?
A. 0
B. 1
C. 2
D. 3
E. 4
Answer: C
Explanation:
The association between a user and Business Unit is shown in a derived table,
BU_GLOBALUSERS. This table carries the BUSINESSUNITKEYand GLOBALUSERKEY.
Structure ofbu_globalusers:
There will be two entries in this table for 101: one for each of the two business units to which 101 belongs.
Reference: Oracle Identity Analytics Database Administrator's Guide
BU_GLOBALUSERS Table