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NEW QUESTION: 1
Hinweis: Diese Frage ist Teil einer Reihe von Fragen, die dasselbe Szenario darstellen. Jede Frage in der Reihe enthält eine eindeutige Lösung, mit der die angegebenen Ziele erreicht werden können. Einige Fragensätze haben möglicherweise mehr als eine richtige Lösung, während andere möglicherweise keine richtige Lösung haben.
Nachdem Sie eine Frage in diesem Abschnitt beantwortet haben, können Sie NICHT mehr darauf zurückkommen. Infolgedessen werden diese Fragen nicht im Überprüfungsbildschirm angezeigt.
Sie müssen eine Lösung empfehlen, um die geplante Änderung für die E-Mail-Adressen der New Yorker Bürovorsteher zu implementieren.
Lösung: Füllen Sie die Attribute in Active Directory aus und implementieren Sie die Verzeichnissynchronisierung.
Erfüllt dies das Ziel?
A. Nein
B. Ja
Answer: A
Explanation:
Erläuterung
Diese Option beeinträchtigt nicht die aktuelle primäre SMTP-Adresse und fügt eine neue primäre SMTP-Adresse hinzu.

NEW QUESTION: 2
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 D
C. Option A
D. Option B
E. Option E
Answer: D
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: 3
お客様は複数のストレージに疲れており、急成長しており、複雑な環境の管理に問題があります。簡素化、管理の容易さ、およびストレージの効率性が重要な要件です。
どの製品がお客様の要件を満たしていますか？
A. IBM Storwize V7000
B. IBMフラッシュシステム900
C. IBM Storwize V5020
D. IBM DS8884
Answer: A

NEW QUESTION: 4
What is the customer advantage of HP 3PAR StoreServ self-configuring, provisioning, and optimizing by autonomic management?
A. Reduces time spent managing storage by 90%
B. Provides non-disruptive data mobility between systems for pain-free refresh and load balancing
C. Reduces capacity requirements by 50% - guaranteed
D. Doubles VM density per server - guaranteed
Answer: A