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NEW QUESTION: 1
A customer wants the ability to track the call types for Automatic Call Distribution (ACD) calls answered by agents. These call types will be defined by the customer.
Which call feature can the customer use to track their defined call types?
A. Feature Access Codes (FAC)
B. Call Work Codes (CWC)
C. Redistribution on No Answer (RONA)
D. Least Occupied Agent (LOA)
Answer: B
Explanation:
Explanation/Reference:
Reference: Avaya Aura™ Call Center Feature Reference 6.0 page 51

NEW QUESTION: 2
IT戦略がビジネス目標をサポートすることを組織の取締役会に保証する最良の方法は、次のとおりです。
A. IT戦略が完全に文書化され、普及していることを確認します。
B. 上級ビジネスマネージャーがIT予算をレビューするようにします。
C. 重要成功要因（CSF）の達成を特定し、悔い改めます。
D. 新しいテクノロジーの定期的な評価を提供します。
Answer: C

NEW QUESTION: 3
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、指定された目標を達成でき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がありますが、他の質問セットには正しい解決策がない場合があります。
このセクションの質問に回答すると、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。
次の表に示すリソースを含むSubscription1という名前のAzureサブスクリプションがあります。

VM1は、NIC1という名前のネットワークインターフェイスを使用して、VNET2という名前の仮想ネットワークに接続します。
VM1用にNIC2という名前の新しいネットワークインターフェイスを作成する必要があります。
解決策：RG1と米国中央部にNIC2を作成します。
これは目標を達成していますか？
A. はい
B. いいえ
Answer: B
Explanation:
The virtual machine you attach a network interface to and the virtual network you connect it to must exist in the same location, here West US, also referred to as a region.
References:
https://docs.microsoft.com/en-us/azure/virtual-network/virtual-network-network-interface

NEW QUESTION: 4
You create an Azure Machine Learning workspace and set up a development environment. You plan to train a deep neural network (DNN) by using the Tensorflow framework and by using estimators to submit training scripts.
You must optimize computation speed for training runs.
You need to choose the appropriate estimator to use as well as the appropriate training compute target configuration.
Which values should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

Explanation:
Box 1: Tensorflow
TensorFlow represents an estimator for training in TensorFlow experiments.
Box 2: 12 vCPU, 112 GB memory..,2 GPU,..
Use GPUs for the deep neural network.
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-train-core/azureml.train.dnn

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