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NEW QUESTION: 1
A customer would like to upgrade their onboard fast Ethernet connection to connect to their new gigabit network at the gigabit speed. Which of the following will enable them to do so?
A. Convert the user to wireless.
B. Update the drivers for the onboard port.
C. Flash the BIOS to unlock the Ethernet port.
D. Install a 10/100/1000 NIC.
Answer: D

NEW QUESTION: 2
What are two characteristics that are associated with the End User and User account types?
(Choose two.)
A. They can utilize LDAP.
B. They can be listed in a directory.
C. The Application is associated with the End User and User account types.
D. They cannot use LDAP.
E. They are used to authorize applications.
Answer: A,C

NEW QUESTION: 3
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You have a Python script named train.py in a local folder named scripts. The script trains a regression model by using scikit-learn. The script includes code to load a training data file which is also located in the scripts folder.
You must run the script as an Azure ML experiment on a compute cluster named aml-compute.
You need to configure the run to ensure that the environment includes the required packages for model training. You have instantiated a variable named aml-compute that references the target compute cluster.
Solution: Run the following code:

Does the solution meet the goal?
A. No
B. Yes
Answer: A
Explanation:
Explanation
The scikit-learn estimator provides a simple way of launching a scikit-learn training job on a compute target. It is implemented through the SKLearn class, which can be used to support single-node CPU training.
Example:
from azureml.train.sklearn import SKLearn
}
estimator = SKLearn(source_directory=project_folder,
compute_target=compute_target,
entry_script='train_iris.py'
)
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-train-scikit-learn