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Implementation Engineer - Data Protection Exam

  • Exam Number/Code : NS0-528
  • Exam Name : Implementation Engineer - Data Protection Exam
  • Questions and Answers : 213 Q&As
  • Update Time: 2019-01-10
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NEW QUESTION: 1
According to the White House Conference for a Drug-Free America, approximately how many Americans had used an illegal drug in 1987?
A. 1 in 2
B. 1 in 7
C. 1 in 40
D. 1 in 20
Answer: B

NEW QUESTION: 2
You have an Exchange Server 2013 organization.
You need to prevent a user named User1 from permanently deleting an email message in
his mailbox.
What should you do on the mailbox of User1?
A. Implement a retention policy.
B. Configure a retention hold.
C. Implement a data loss prevention (DLP) policy.
D. Configure an in-place hold.
Answer: D
Explanation:
In Exchange 2013, you can use In-Place Hold to accomplish the following goals: Place user mailboxes on hold and preserve mailbox items immutably Preserve mailbox items deleted by users or automatic deletion processes such as MRM Use query-based In-Place Hold to search for and retain items matching specified criteria Preserve items indefinitely or for a specific duration Place a user on multiple holds for different cases or investigations Keep In-Place Hold transparent from the user by not having to suspend MRM Enable In-Place eDiscovery searches of items placed on hold

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