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NEW QUESTION: 1
The data that is available with the optimizing process gives us a new perspective on testing.
A. False
B. True
Answer: B

NEW QUESTION: 2
A Work Breakdown Structure numbering system
A. Presents justification for the project
B. Determines the level at which individual WBS elements are found
C. Helps in automating the WBS using appropriate software
D. Determines the complexity of the project
Answer: B

NEW QUESTION: 3
In a ten-disk RAID 0 set, each disk has a Mean Time Between Failure (MTBF) of 100,000 hours. What is the MTBF of the set?
A. 10,000 hours
B. 1,000 hours
C. 10 hours
D. 1,000,000 hours
Answer: A

NEW QUESTION: 4
Azure Container Instanceにモデルをデプロイします。
モデルAPIを呼び出すには、Azure Machine Learning SDKを使用する必要があります。
ネイティブSDKクラスおよびメソッドを使用して、デプロイされたモデルを呼び出す必要があります。
コマンドをどのように完了する必要がありますか？回答するには、回答領域で適切なオプションを選択します。
注：それぞれの正しい選択は1ポイントの価値があります。

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