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NEW QUESTION: 1
Hinweis: Diese Frage ist Teil einer Reihe von Fragen, die dasselbe Szenario darstellen. Jede Frage in der Reihe enthält eine eindeutige Lösung, mit der die angegebenen Ziele erreicht werden können. Einige Fragensätze haben möglicherweise mehr als eine richtige Lösung, während andere möglicherweise keine richtige Lösung haben.
Nachdem Sie eine Frage in diesen Abschnitten beantwortet haben, können Sie NICHT mehr darauf zurückkommen. Infolgedessen werden diese Fragen nicht im Überprüfungsbildschirm angezeigt.
Ihr Netzwerk enthält eine Active Directory-Domäne mit dem Namen contoso.com. Die Domäne enthält einen Mitgliedsserver mit dem Namen Server1, auf dem Windows Server 2016 ausgeführt wird und auf dem die DNS-Serverrolle installiert ist. Das automatische Aufräumen von Statusdatensätzen ist aktiviert und der Aufräumzeitraum auf 10 Tage festgelegt.
Alle Client-Computer registrieren ihre Namen dynamisch in der DNS-Zone von contoso.com auf Server1.
Sie stellen fest, dass die Namen mehrerer Clientcomputer, die vor einigen Wochen aus dem Netzwerk entfernt wurden, weiterhin aufgelöst werden können.
Sie müssen Server1 so konfigurieren, dass die Datensätze der Clientcomputer, die länger als 10 Tage offline waren, automatisch entfernt werden.
Lösung: Sie legen den TTL-Wert (Time to Live) aller Datensätze in der Zone fest.
Erfüllt dies das Ziel?
A. Ja
B. Nein
Answer: B
Explanation:
Erläuterung
https://technet.microsoft.com/en-us/library/cc958972.aspx
NEW QUESTION: 2
What is the principle function of the data center core layer?
A. services layer
B. repeater
C. QoS marking
D. access control
E. high-speed packet switching
F. firewalling and intrusion prevention
Answer: E
Explanation:
Explanation/Reference:
Explanation:
NEW QUESTION: 3
モデルのトレーニング要件に従って、順列機能の重要度モジュールを設定する必要があります。
どのプロパティを選択する必要がありますか？回答するには、回答エリアで適切なオプションを選択します。
注：それぞれの正しい選択には1ポイントの価値があります。
Answer:
Explanation:
Explanation:
Box 1: Accuracy
Scenario: You want to configure hyperparameters in the model learning process to speed the learning phase by using hyperparameters. In addition, this configuration should cancel the lowest performing runs at each evaluation interval, thereby directing effort and resources towards models that are more likely to be successful.
Box 2: R-Squared