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NEW QUESTION: 1
You deploy a model in Azure Container Instance.
You must use the Azure Machine Learning SDK to call the model API.
You need to invoke the deployed model using native SDK classes and methods.
How should you complete the command? To answer, select the appropriate options in the answer areas.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

Explanation:
Box 1: from azureml.core.webservice import Webservice
The following code shows how to use the SDK to update the model, environment, and entry script for a web service to Azure Container Instances:
from azureml.core import Environment
from azureml.core.webservice import Webservice
from azureml.core.model import Model, InferenceConfig
Box 2: predictions = service.run(input_json)
Example: The following code demonstrates sending data to the service:
import json
test_sample = json.dumps({'data': [
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
[10, 9, 8, 7, 6, 5, 4, 3, 2, 1]
]})
test_sample = bytes(test_sample, encoding='utf8')
prediction = service.run(input_data=test_sample)
print(prediction)
Reference:
https://docs.microsoft.com/bs-latn-ba/azure/machine-learning/how-to-deploy-azure-container-instance
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-deployment

NEW QUESTION: 2
圧縮にはどのコンポーネントが必要ですか？
A. PFDC
B. シックLUN
C. FAST Cache
D. FAST VP
Answer: B
Explanation:
説明
参考文献：https://www.emc.com/collat​​eral/white-papers/h15571-dell-emc-unity-compression.pdf

NEW QUESTION: 3
You have been tasked with designing a data cleansing process that does NOT write any Interim data or results In Enterprise Data Quality (EDQ). How should you proceed?
A. Stage all results views and export them when running the |ob.
B. Design the process to read and write data from web services interfaces and mi* it in Interval mode.
C. Design aJob in EDQ and deploy it directly to the data source using a DIPC agent.
D. Configure a Job without any whole record set processors, disableall data staging, and ensure all processes are writingno drilldown data.
Answer: A

NEW QUESTION: 4
複数のAzure仮想マシンをデプロイすることを計画しています。
単一のデータセンターに障害が発生した場合に、仮想マシンで実行されているサービスが利用可能であることを確認する必要があります。
解決策：仮想マシンをスケールセットにデプロイします。これは目標を達成していますか？
A. はい
B. いいえ
Answer: B
Explanation:
Azure virtual machine scale sets let you create and manage a group of load balanced VMs. The number of VM instances can automatically increase or decrease in response to demand or a defined schedule. Scale sets provide high availability to your applications, and allow you to centrally manage, configure, and update many VMs.
Virtual machines in a scale set can be deployed across multiple update domains and fault domains to maximize availability and resilience to outages due to data center outages, and planned or unplanned maintenance events.
https://docs.microsoft.com/en-us/azure/virtual-machine-scale-sets/availability