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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 einzigartige Lösung, die die angegebenen Ziele erreichen kann. 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.
Ihr Netzwerk enthält eine lokale Active Directory-Domäne. Die Domäne enthält 2.000 Computer, auf denen Windows 8.1 ausgeführt wird und auf denen Anwendungen installiert sind (siehe folgende Tabelle).
Sie registrieren alle Computer in Upgrade Readiness.
Sie müssen sicherstellen, dass App1 und App2 den UpgradeDecision-Status Bereit zum Aktualisieren haben.
Lösung: Sie setzen den Wichtigkeitsstatus von App2 auf Niedrige Installationsanzahl.
Erfüllt dies das Ziel?
A. Ja
B. Nein
Answer: A
Explanation:
Explanation
If an app is installed on less than 2% of the targeted devices, it's marked Low install count. Two percent is the default value. You can adjust the threshold in the readiness settings from 0% to 10%. Desktop Analytics automatically marks these apps as Ready to upgrade.
Reference:
https://docs.microsoft.com/en-us/configmgr/desktop-analytics/about-deployment-plans
NEW QUESTION: 2
_________ component under MRP is used to flag the materials which affect the company
profits and also takes the critical resources.
A. None of the above
B. Master Production
C. Master Production Scheduling
D. Master Scheduling
Answer: 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