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NEW QUESTION: 1
A customer is deploying a mission-critical Oracle database with high SLA requirements, including high performance and high availability. The customer has chosen to purchase an All-Flash vSAN solution.
Which three storage policies should be used? (Choose three.)
A. IOPS limit and checksum should be enabled.
B. RAID5/6 for data disk and RAID1 for OS disk with FTT=2.
C. Deduplication and Compression should be disabled.
D. Configure multiple disk stripes.
E. RAID5/6 for OS disk and RAID1 for data disk with FTT=2.
Answer: A,D,E
NEW QUESTION: 2
If you are running an application that has strict legal requirements that the data cannot reside on systems that contain other applications or systems, which aspect of cloud computing would be prohibitive in this case?
A. Multitenancy
B. Portability
C. Broad network access
D. Elasticity
Answer: A
Explanation:
Multitenancy is the aspect of cloud computing that involves having multiple customers and applications running within the same system and sharing the same resources. Although considerable mechanisms are in place to ensure isolation and separation, the data and applications are ultimately using shared resources. Broad network access refers to the ability to access cloud services from any location or client.
Portability refers to the ability to easily move cloud services between different cloud providers, whereas elasticity refers to the capabilities of a cloud environment to add or remove services, as needed, to meet current demand.
NEW QUESTION: 3
Azure Machine Learning Designerを使用して、機械学習モデルを作成します。モデルをAzureKubernetes Service（AKS）推論コンピューティングクラスターでリアルタイムサービスとして公開します。デプロイされたエンドポイント構成に変更を加えることはありません。
アプリケーション開発者に、エンドポイントを使用するために必要な情報を提供する必要があります。
アプリケーション開発者に提供する必要がある2つの値はどれですか？それぞれの正解は、解決策の一部を示しています。
注：正しい選択はそれぞれ1ポイントの価値があります。
A. エンドポイントの推論パイプライン実験の実行ID。
B. エンドポイントのURL。
C. エンドポイントのキー。
D. エンドポイントがホストされているAKSクラスターの名前。
E. エンドポイントの推論パイプラインの名前。
Answer: B,C
Explanation:
Deploying an Azure Machine Learning model as a web service creates a REST API endpoint. You can send data to this endpoint and receive the prediction returned by the model.
You create a web service when you deploy a model to your local environment, Azure Container Instances, Azure Kubernetes Service, or field-programmable gate arrays (FPGA). You retrieve the URI used to access the web service by using the Azure Machine Learning SDK. If authentication is enabled, you can also use the SDK to get the authentication keys or tokens.
Example:
# URL for the web service
scoring_uri = '<your web service URI>'
# If the service is authenticated, set the key or token
key = '<your key or token>'
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-consume-web-service