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NEW QUESTION: 1
圧縮オプションと暗号化オプションの両方を使用してテーブルを作成した場合、どのアクティビティが発生しますか？
A. 表領域データが暗号化される前に圧縮が実行されます。
B. COMPRESSIONオプションとENCRYPTIONオプションを一緒に使用できないため、システムはエラーを生成します。
C. 圧縮が実行され、 '' data_at_rest_flag "がオンになり、テーブルスペースデータが暗号化されます。
D. 最初にテーブルスペースデータが暗号化され、次に圧縮が実行されます。
Answer: A
Explanation:
If a table is created with both the COMPRESSION and ENCRYPTION options, compression is performed before tablespace data is encrypted.
https://docs.oracle.com/cd/E17952_01/mysql-8.0-en/innodb-data-encryption.html

NEW QUESTION: 2
Your Azure Machine Learning workspace has a dataset named real_estate_dat a. 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: 3
Refer to the exhibit.

How many EIGRP routes will appear in the routing table of R2?
A. 0
B. 1
C. 2
D. 3
Answer: A
Explanation:
EIGRPv6 on R2 was shut down so there is no EIGRP routes on the routing table of R2. If we turn on EIGRPv6 on R2 (with "no shutdown" command) then we would see the prefix of the loopback interface of R1 in the routing table of R2.

Note. EIGRPv6 requires the "ipv6 unicast-routing" global command to be turned on first or it will not work.