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NEW QUESTION: 1

A. Option D
B. Option B
C. Option C
D. Option A
Answer: C

NEW QUESTION: 2
View the exhibit.

Assuming the tag library in the exhibit is imported with the prefix stock, which custom tag Invocation outputs the contents of the variable exposed by the quote tag?
A. <stock:quote symbol= "SUNW" var= "quote"><%= quote %></stock:quote>
B. <stock:quote symbol= "SUNW" var= "quote" />$ {quote}
C. ${var}<stock:quote symbol= "SUNW" />
D. <stock:quote symbol= "SUNW">$ {var}</stock:quote>
Answer: A
Explanation:
Explanation
<name-from-attribute> is to create the variable name from attribute name (the variable named var value, that is, quote)
<rtexprvalue> represents the property whether or not to accept the scriptlet expressions of calculations, the default case is false, that can only accept static values , Tag to set the value of the variable var = quote, $ {quote} EL expression output quote variable.

NEW QUESTION: 3
You need to build a model that looks at the probability of an outcome. You must regulate between L1 and L2.
Which classification method should you use?
A. Two-Class Neural Network
B. Two-Class Decision Forest
C. Two-Class Logistic Regression
D. Two-Class Support Vector Machine
Answer: A

NEW QUESTION: 4
You use the Azure Machine Learning service to create a tabular dataset named training.data. You plan to use this dataset in a training script.
You create a variable that references the dataset using the following code:
training_ds = workspace.datasets.get("training_data")
You define an estimator to run the script.
You need to set the correct property of the estimator to ensure that your script can access the training.data dataset Which property should you set?
A)

B)

C)

D)

A. Option D
B. Option C
C. Option B
D. Option A
Answer: D
Explanation:
Explanation
Example:
# Get the training dataset
diabetes_ds = ws.datasets.get("Diabetes Dataset")
# Create an estimator that uses the remote compute
hyper_estimator = SKLearn(source_directory=experiment_folder,
inputs=[diabetes_ds.as_named_input('diabetes')], # Pass the dataset as an input compute_target = cpu_cluster, conda_packages=['pandas','ipykernel','matplotlib'], pip_packages=['azureml-sdk','argparse','pyarrow'], entry_script='diabetes_training.py') Reference:
https://notebooks.azure.com/GraemeMalcolm/projects/azureml-primers/html/04%20-%20Optimizing%20Model