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NEW QUESTION: 1
企業はAIビルダーを実装する方法を評価しています。
AIビルダーを使用できるのはどの2つのシナリオですか？正しい選択ごとに完全なソリューションが提示されます。
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。
A. 外部データベースのデータを同期します。
B. サービスに登録しているすべてのユーザーにメールを送信します。
C. 画像を解釈し、画像に対してアクションを実行します。
D. 複数のデータソースからデータを収集し、トレンドデータを示すダッシュボードを表示します。
Answer: A,C
Explanation:
Reference:
https://powerapps.microsoft.com/en-us/blog/introducing-ai-builder-for-powerplatform/

NEW QUESTION: 2
Your Dataproc cluster runs in a single Virtual Private Cloud (VPC) network in a single subnet with range
172.16.20.128/25. There are no private IP addresses available in the VPC network. You want to add new VMs to communicate with your cluster using the minimum number of steps. What should you do?
A. Create a new VPC network for the VMs with a subnet of 172.32.0.0/16. Enable VPC network Peering between the Dataproc VPC network and the VMs VPC network. Configure a custom Route exchange.
B. Modify the existing subnet range to 172.16.20.0/24.
C. Create a new Secondary IP Range in the VPC and configure the VMs to use that range.
D. Create a new VPC network for the VMs. Enable VPC Peering between the VMs' VPC network and the Dataproc cluster VPC network.
Answer: D
Explanation:
Explanation
A subnet has a single primary IP address range and, optionally, one or more secondary IP address ranges. For each subnet IP address range, Google Cloud creates a subnet route. When you use VPC Network Peering, Google Cloud always exchanges the subnet routes that don't use privately reused public IP addresses between the two peered networks. If firewall rules in each network permit communication, VM instances in one network can communicate with instances in the peered network.

NEW QUESTION: 3

A. SUBSCRIBE
B. REGISTER
C. NOTIFY
D. INFO
Answer: A

NEW QUESTION: 4
Azure Machine Learning Studioを使用して機械学習実験を構築します。
データを2つの異なるデータセットに分割する必要があります。
どのモジュールを使用する必要がありますか？
A. データの分割
B. データをビンにグループ化
C. データをクラスターに割り当てる
D. トレーニング済みモデルのロード
Answer: B
Explanation:
The Group Data into Bins module supports multiple options for binning data. You can customize how the bin edges are set and how values are apportioned into the bins.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/group-data-into-bins