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NEW QUESTION: 1
You have an Azure Machine Learning workspace that contains a CPU-based compute cluster and an Azure Kubernetes Services (AKS) inference cluster. You create a tabular dataset containing data that you plan to use to create a classification model.
You need to use the Azure Machine Learning designer to create a web service through which client applications can consume the classification model by submitting new data and getting an immediate prediction as a response.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

Answer:
Explanation:

Explanation:
Step 1: Create and start a Compute Instance
To train and deploy models using Azure Machine Learning designer, you need compute on which to run the training process, test the model, and host the model in a deployed service.
There are four kinds of compute resource you can create:
Compute Instances: Development workstations that data scientists can use to work with data and models.
Compute Clusters: Scalable clusters of virtual machines for on-demand processing of experiment code.
Inference Clusters: Deployment targets for predictive services that use your trained models.
Attached Compute: Links to existing Azure compute resources, such as Virtual Machines or Azure Databricks clusters.
Step 2: Create and run a training pipeline..
After you've used data transformations to prepare the data, you can use it to train a machine learning model. Create and run a training pipeline Step 3: Create and run a real-time inference pipeline After creating and running a pipeline to train the model, you need a second pipeline that performs the same data transformations for new data, and then uses the trained model to inference (in other words, predict) label values based on its features. This pipeline will form the basis for a predictive service that you can publish for applications to use.
Reference:
https://docs.microsoft.com/en-us/learn/modules/create-classification-model-azure-machine-learning-designer/

NEW QUESTION: 2
Which command configures the management IP address on a WLAN controller?
A. ip address 192.168.0.100 24
B. oap management-ip 192.168.0.100 slot 1
C. management-ip address 192.168.0.100 255.255.255.0
D. interface ip address 192.168.0.100 slot 1
Answer: B
Explanation:
Reference:http://h20565.www2.hp.com/portal/site/hpsc/template.BINARYPORTLET/public/kb/doc Display/resource.process/?javax.portlet.begCacheTok=com.vignette.cachetoken&javax.portlet.en dCacheTok=com.vignette.cachetoken&javax.portlet.rid_ba847bafb2a2d782fcbb0710b053ce01=do cDisplayResURL&javax.portlet.rst_ba847bafb2a2d782fcbb0710b053ce01=wsrpresourceState%3DdocId%253Demr_na-c037195575%257CdocLocale%253Den_US&javax.portlet.tpst=ba847bafb2a2d782fcbb0710b053ce01_ws_B I&ac.admitted=1401095294880.876444892.492883150(page 4)
Explanation:
Parameters
ip-address: Specifies the management IP address of the OAP module. This address must be
configured
on the OAP module.
slot slot-number: Specifies the number of the slot where the switching engine resides. The value of
this
field must be 0.
Usage guidelines
To use an NMS to manage the switching engine, you must first configure a management IP
address for
the engine.
Examples
# Set the management IP address to 192.168.0.101 for the switching engine of the HP 830 Series
PoE+
Unified Wired-WLAN Switch.
<Sysname> system-view
[Sysname] oap management-ip 192.168.0.101 slot 0

NEW QUESTION: 3
Ein Produktionsprozess endet.
Was können Sie tun, um die fertigen Waren dem Inventar hinzuzufügen? Hinweis: Auf diese Frage gibt es 2 richtige Antworten.
A. Fügen Sie einen Beleg aus dem Produktionsdokument für den Produktionsauftrag hinzu
B. Schließen Sie den Fertigungsauftrag
C. Wählen Sie im Kontextmenü des Fertigungsauftrags die Option Report Completion
D. Ändern Sie den Status des Fertigungsauftrags in Freigegeben
Answer: A,C