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NEW QUESTION: 1
On a Cisco switch, which protocol determines if an attached VoIP phone is from Cisco or from another vendor?
A. TCP
B. RTP
C. UDP
D. CDP
Answer: D
Explanation:
http://computernetworkingnotes.com/cisco-devices-administration-and-configuration/ciscodiscoveryprotocol.html The Cisco Unified IP Phone uses CDP to communicate information such as auxiliary VLAN ID, per port power management details, and Quality of Service (QoS) configuration information with the Cisco Catalyst switch. Cisco Discovery Protocol (CDP) is a proprietary protocol designed by Cisco to help administrators collect information about both locally attached and remote devices. By using CDP, you can gather hardware and protocol information about neighbor devices, which is useful info for troubleshooting the network. CDP messages are generated every 60 seconds as multicast messages on each of its active interfaces. The information shared in a CDP packet about a Cisco device includes the following: Name of the device configured with the hostname command IOS software version Hardware capabilities, such as routing, switching, and/or bridging Hardware platform, such as 2600, 2950, or 1900 The layer-3 address(es) of the device The interface the CDP update was generated on

NEW QUESTION: 2
ブレードサーバは、4つの電源装置を搭載するシャーシで最大電力で2000 Wを消費します。 次のうちどれがブレードシャーシに電力を供給するために必要な900W電源装置の最小量ですか？
A. One
B. Two
C. Three
D. Four
Answer: C

NEW QUESTION: 3





A. Option D
B. Option B
C. Option A
D. Option C
Answer: A
Explanation:
References: http://msdn.microsoft.com/en-us/library/ms187348.aspx

NEW QUESTION: 4
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