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NEW QUESTION: 1
Which two components are part of storage I/O multipathing on ESXi? (Choose two.)
A. SATP
B. PSP
C. VASA
D. VAAI
Answer: A,B
Explanation:
Explanation/Reference:
Explanation:
The NMP associates a set of physical paths with a specific storage device, or LUN. The specific details of handling path failover for a given storage array are delegated to a Storage Array Type Plug-In (SATP). The specific details for determining which physical path is used to issue an I/O request to a storage device are handled by a Path Selection Plug-In (PSP). SATPs and PSPs are sub plug-ins within the NMP module.
Reference https://pubs.vmware.com/vsphere-50/index.jsp?topic=%
2Fcom.vmware.vsphere.storage.doc_50%2FGUID-9DED1F73-7375-4957-BF69-41B56C3E5224.html

NEW QUESTION: 2
HOTSPOT
Your company has four offices. The offices are located in Montreal, Seattle, New York, and Miami.
Users access all of the web-based resources by using web proxy servers. The IP addresses of the web proxies at each office are configured as shown in the following table.

The connections to the web proxies are balanced by using round-robin DNS.
The company plans to deploy a new application. The new application has a farm of front-end web servers that connect to a back-end application server. When a session to a web server is established, the web server stores data until the session closes. Once the session closes, the data is sent to the application server.
You need to ensure that the incoming sessions to the web server farm are distributed among the web servers. The solution must ensure that if a web server fails, the users are NOT directed to the failed server.
How should you configure the port rule? To answer, select the appropriate options in the answer area.
Hot Area:

Answer:
Explanation:

NEW QUESTION: 3
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: 4
The securities repurchased have the same stated interest rate as, and maturities similar to, the securities sold and are generally priced to result in substantially the same yield is known as:
A. None of the above
B. Fixed-coupon agreement
C. Yield-maintenance agreements
D. Variable-coupon a agreements
Answer: B