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NEW QUESTION: 1
過度の製品拒否率を評価する監査人は調査する必要があります。
I.
返品に関する販売部門と生産部門間のコミュニケーション。
II。
年初来と比較した年初来の製品販売量。
III。
顧客の信用格付けとそれらの顧客への売上の変化。
IV。
詳細な製品スクラップのアカウントと蓄積。
A. I、II、III、およびIV。
B. IとIVのみ
C. IとIIIのみ
D. II、III、およびIVのみ
Answer: B

NEW QUESTION: 2
You have an application that contains the following class definitions.

You need to ensure that the Customers class can be initialized by using the following code.

Which code should you add to the application? To answer, drag the appropriate values to the correct targets. Each value may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

NEW QUESTION: 3
You create a deep learning model for image recognition on Azure Machine Learning service using GPU-based training.
You must deploy the model to a context that allows for real-time GPU-based inferencing.
You need to configure compute resources for model inferencing.
Which compute type should you use?
A. Field Programmable Gate Array
B. Machine Learning Compute
C. Azure Container Instance
D. Azure Kubernetes Service
Answer: D
Explanation:
Explanation
You can use Azure Machine Learning to deploy a GPU-enabled model as a web service. Deploying a model on Azure Kubernetes Service (AKS) is one option. The AKS cluster provides a GPU resource that is used by the model for inference.
Inference, or model scoring, is the phase where the deployed model is used to make predictions. Using GPUs instead of CPUs offers performance advantages on highly parallelizable computation.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-deploy-inferencing-gpus