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NEW QUESTION: 1
A. Option B
B. Option C
C. Option D
D. Option A
Answer: D
NEW QUESTION: 2
John works as a professional Ethical Hacker. He has been assigned the project of testing the security of www.we-are-secure.com. In order to do so, he performs the following steps of the preattack phase successfully:
Information gathering
Determination of network range
Identification of active systems
Location of open ports and applications
Now, which of the following tasks should he perform next?
A. Install a backdoor to log in remotely on the We-are-secure server.
B. Fingerprint the services running on the we-are-secure network.
C. Map the network of We-are-secure Inc.
D. Perform OS fingerprinting on the We-are-secure network.
Answer: D
NEW QUESTION: 3
호텔 관리 응용 프로그램은 출입문에 전자식 룸 키가 사용될 때마다 로그를 생성하여 입장 요청의 승인 여부를 나타냅니다. 거부된 항목 수가 분당 평균 1 개를 초과한후 통지는 요율이 초과된후 몇분 내에 수신되어야 합니다.
솔루션 아키텍트는 코드를 변경하지 않고 이 요구 사항을 충족하는 솔루션을 어떻게 설계해야 합니까?
A. 승인된 거부 플래그를 메시지 속성으로 하여 Amazon SNS에 직접 로그 전송 구독 필터 정책을 사용하여 거부 알림만 전송
B. 거부를 위해 Amazon CloudWatch Logs 필터로 로그 보내기 속도가 초과되면 경보 생성 알림에 Amazon SNS 사용
C. Amazon Kinesis Data 스트림에 샌드 이벤트. Kinesis 클라이언트 라이브러리 애플리케이션을 작성하여 이벤트 카운트 거부를 소비하고 알림 메시지를 Amazon SNS에 보냅니다.
D. 속도가 초과되면 AWS Lambda 함수를 트리거하도록 구성된 Amazon SQS 대기열로 이벤트를 보냅니다. Lambda가 Amazon SNS에 알림 메시지를 보냅니다.
Answer: B
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 Stream Analytics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
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