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NEW QUESTION: 1
GPUベースのトレーニングを使用して、Azure Machine Learningサービスで画像認識のためのディープラーニングモデルを作成します。
リアルタイムのGPUベースの推論を可能にするコンテキストにモデルをデプロイする必要があります。
モデル推論のためにコンピューティングリソースを構成する必要があります。
どの計算タイプを使用する必要がありますか？
A. 機械学習コンピューティング
B. Azure Kubernetesサービス
C. Azureコンテナーインスタンス
D. フィールドプログラマブルゲートアレイ
Answer: B
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

NEW QUESTION: 2
두 명의 축구 선수가 생산 한 전력을 비교하고 싶습니다. 다음 수식 중 어느 것을 사용해야 합니까?
A. 힘 / 시간
B. 반복 x 중량 해제
C. (강제 x 거리) / 시간
D. (무게 들어 올린 x 가속) / 반복
Answer: C

NEW QUESTION: 3
A high user load and interactive reporting system on a distributed installation is throwing timeout error messages.
How is this resolved?
A. Increase the JVM heap settings in IBM Cognos Configuration under Services.
B. Edit the install_location/webapps/p2pd/WEB-INF/services reportservice.xml and increase the async_wait_timeout_ms, save and restart the service.
C. Edit the install_location/bin CQEConfig.xml and change the DB_Connection_Pool value to 120, save and restart the service.
D. Increase the number of Report Processes under IBM Cognos Administration on the Status tab under System, set properties to equal 2 times the number of total processor cores.
Answer: B
Explanation:
Increase Asynchronous Timeout in High User Load Environments.
If you have a high user load (over 165 users) and interactive reports are running continuously in a distributed installation, you may want to increase the asynchronous timeout setting to avoid getting error messages. The default is 30000.
To resolve this problem, increase the wait timeout.
Procedure
References:
https://www.ibm.com/support/knowledgecenter/en/SSEP7J_11.0.0/com.ibm.swg.ba.cognos
.inst_cr_winux.doc/t_increaseforhighendload.html