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NEW QUESTION: 1
Which log level provides the highest level of detail?
A. WARNING
B. FINE
C. CONFIG
D. INFO
Answer: B

NEW QUESTION: 2
사용자는 CPU 사용률이 70 % 이상일 때 확장되고 CPU 사용률이 30 % 미만일 때 축소되는 AutoScaling을 구성하려고 합니다.
사용자는 위에서 언급 한 조건에 대해 AutoScaling을 어떻게 구성 할 수 있습니까?
A. 조건 동안 원하는 용량을 수동으로 수정하여 AutoScaling 사용
B. 로드 증가 또는 감소시 AutoScaling에 알리도록 ELB 구성
C. 정책과 함께 동적 AutoScaling 사용
D. 일정과 함께 AutoScaling 사용
Answer: C
Explanation:
The user can configure the AutoScaling group to automatically scale up and then scale down based on the specified conditions. To configure this, the user must setup policies which will get triggered by the CloudWatch alarms.
http://docs.aws.amazon.com/AutoScaling/latest/DeveloperGuide/as-scale-based-on-demand.html

NEW QUESTION: 3
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 Functions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. はい
B. いいえ
Answer: B
Explanation:
Explanation
Instead use Azure Stream Analytics and REST API.
Note. Available in both the cloud and Azure IoT Edge, Azure Stream Analytics offers built-in machine learning based anomaly detection capabilities that can be used to monitor the two most commonly occurring anomalies:
temporary and persistent.
Stream Analytics supports user-defined functions, via REST API, that call out to Azure Machine Learning endpoints.
References:
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-machine-learning-anomaly-detection