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NEW QUESTION: 1
Which of the following is a method for apportioning resources that involves setting guaranteed minimums for all tenants/customers within the environment?
Response:
A. Cancellations
B. Limits
C. Reservations
D. Shares
Answer: C
NEW QUESTION: 2
次のコードがあります。
「A」で始まるすべての車両を表示する必要があります。
どのようにコードを完成させる必要がありますか?回答するには、適切なコード要素を正しいターゲットにドラッグします。各コード要素は、1回、複数回、またはまったく使用しない場合があります。ペイン間で分割バーをドラッグするか、コンテンツを表示するにはスクロールする必要がある場合があります。
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:
NEW QUESTION: 3
A. Option B
B. Option C
C. Option D
D. Option A
Answer: C
NEW QUESTION: 4
注:この質問は、同じシナリオを提示する一連の質問の一部です。シリーズの各質問には、記載された目標を達成する可能性のある独自のソリューションが含まれています。一部の質問セットには複数の正しい解決策がある場合もあれば、正しい解決策がない場合もあります。
質問に答えた後、その質問に戻ることはできません。その結果、これらの質問はレビュー画面に表示されません。
ストリーミングデータを生成するAzure IoT Edgeデバイスがあります。
デバイスでは、Azure Machine Learningモデルを使用してデータの異常を検出する必要があります。異常が検出されたら、デバイスは異常に関する情報をAzure IoT Hubストリームに追加する必要があります。
ソリューション:Azure Machine LearningモデルをIoT Edgeモジュールとしてデプロイします。
これは目標を達成していますか?
A. いいえ
B. はい
Answer: B
Explanation:
Explanation
You can use IoT Edge modules to deploy code that implements your business logic directly to your IoT Edge devices. For example, you can deploy an Azure Machine Learning module that predicts when a device fails based on simulated machine temperature data.
References:
https://docs.microsoft.com/bs-latn-ba/azure/iot-edge/tutorial-deploy-machine-learning