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NEW QUESTION: 1
Which four options are the HTTP methods supported by a reset API?
A. RETRIEVE
B. POST
C. COPY
D. PUT
E. GET
F. DELETE
G. SET
Answer: B,D,E,F

NEW QUESTION: 2
Sie erstellen ein Login mit dem Namen BIAppUser. Die Anmeldung muss auf die Berichtsdatenbank zugreifen können.
Sie müssen den Zugriff auf das BIAppUser-Login in der Datenbank gewähren.
Wie sollen Sie die Transact-SQL-Anweisungen vervollständigen? Ziehen Sie zum Beantworten die entsprechenden Transact-SQL-Segmente an die richtigen Positionen. Jedes Transact-SQL-Segment kann einmal, mehrmals oder gar nicht verwendet werden. Möglicherweise müssen Sie die geteilte Leiste zwischen den Fenstern ziehen oder einen Bildlauf durchführen, um den Inhalt anzuzeigen.

Answer:
Explanation:

Explanation

Box 1: Reporting
The user is to be created in the Reporting database.
Box 2: CREATE USER
Box 3: FOR LOGIN [BIAppUser]
Users are created per database and are associated with logins. You must be connected to the database in where you want to create the user. Here is some sample Transact-SQL that creates a user:
CREATE USER readonlyuser FROM LOGIN readonlylogin;
References: https://azure.microsoft.com/en-us/blog/adding-users-to-your-sql-azure-database/

NEW QUESTION: 3
You create a datastore named training_data that references a blob container in an Azure Storage account. The blob container contains a folder named in which multiple comma-separated values (CSV) files are stored.
You have a script named train.py in a local folder named ./script that you plan to run as an experiment using an estimator. The script includes the following code to read data from the csv_files folder:

You have the following script.

You need to configure the estimator for the experiment so that the script can read the data from a data reference named data_ref that references the csv_files folder in the training_data datastore.
Which code should you use to configure the estimator?

A. Option C
B. Option A
C. Option B
D. Option E
E. Option D
Answer: C
Explanation:
Explanation
Besides passing the dataset through the inputs parameter in the estimator, you can also pass the dataset through script_params and get the data path (mounting point) in your training script via arguments. This way, you can keep your training script independent of azureml-sdk. In other words, you will be able use the same training script for local debugging and remote training on any cloud platform.
Example:
from azureml.train.sklearn import SKLearn
script_params = {
# mount the dataset on the remote compute and pass the mounted path as an argument to the training script
'--data-folder': mnist_ds.as_named_input('mnist').as_mount(),
'--regularization': 0.5
}
est = SKLearn(source_directory=script_folder,
script_params=script_params,
compute_target=compute_target,
environment_definition=env,
entry_script='train_mnist.py')
# Run the experiment
run = experiment.submit(est)
run.wait_for_completion(show_output=True)
Reference:
https://docs.microsoft.com/es-es/azure/machine-learning/how-to-train-with-datasets

NEW QUESTION: 4
Drag and drop the functionalities from the left onto the correct target fields on the right.

Answer:
Explanation: