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NEW QUESTION: 1
Netcool MIB Managerに関して正しい3つのステートメントはどれですか？
A. インポートされたSNMPオブジェクトの一部またはすべてをNetcoolルールファイルおよびその他のファイル形式にエクスポートできます。
B. MIBデータのインポートとルールファイルのエクスポートに使用できるコマンドラインインターフェイスがあります。
C. MIBの依存関係を解決し、WebGUIを使用してオンラインで結果を表示するためのWebGUIインターフェイスがあります。
D. WebGUIインターフェースからアクセスできます。
E. SNMP MIBファイルをインポートし、MIBの依存関係を解決して、オブジェクト識別子（OlD）ツリーを構築します。
F. Syslogファイルをインポートし、汎用プローブで使用するルールファイルを生成できます。
Answer: B,E,F
NEW QUESTION: 2
Refer to the exhibit.
If CDP is enabled on all devices and interfaces, which devices will appear in the output of a show cdp neighbors command issued from R2?
A. R1, S1, S2, R3, and S3
B. R1 and R3
C. R1, S1, S2, and R3
D. R2 and R3
E. R3 and S2
Answer: E
Explanation:
Explanation/Reference:
A Cisco device enabled with CDP sends out periodic interface updates to a multicast address in order to make itself known to neighbors. Since it is a layer two protocol, these packets are not routed. So the devices detected would be immediate connected neighbors.
NEW QUESTION: 3
You have downloaded and installed the gcloud command line interface (CLI) and have authenticated with your Google Account. Most of your Compute Engine instances in your project run in the europe-west1-d zone.
You want to avoid having to specify this zone with each CLI command when managing these instances. What should you do?
A. In the CLI installation directory, create a file called default.conf containing zone=europe-west1-d.
B. Set the europe-west1-d zone as the default zone using the gcloud config subcommand.
C. In the Settings page for Compute Engine under Default location, set the zone to europe-west1-d.
D. Create a Metadata entry on the Compute Engine page with key compute/zone and value europe-west1-d.
Answer: A
NEW QUESTION: 4
組織はAzure Machine Learningサービスを使用しており、機械学習の使用を拡大したいと考えています。
次のコンピューティング環境があります。組織は別のコンピューティング環境を作成したくありません。
次のシナリオで使用するコンピューティング環境を決定する必要があります。
どの計算タイプを使用する必要がありますか？答えるには、適切なコンピューティング環境を正しいシナリオにドラッグします。各コンピューティング環境は、1回、複数回、またはまったく使用しない場合があります。コンテンツを表示するには、ペイン間で分割バーをドラッグするか、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:
Explanation:
Box 1: nb_server
Box 2: mlc_cluster
With Azure Machine Learning, you can train your model on a variety of resources or environments, collectively referred to as compute targets. A compute target can be a local machine or a cloud resource, such as an Azure Machine Learning Compute, Azure HDInsight or a remote virtual machine.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/concept-compute-target
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-set-up-training-targets