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NEW QUESTION: 1
A company requires switches to use Multiple Spanning Tree Protocol (MSTP) instead of Rapid Spanning Tree Protocol (RSTP), so that the switches can forward traffic on more than one switch-to-switch link. How does the administrator set up the solution to meet this requirement?
A. Set the same spanning tree cost on all redundant links on a switch.
B. Ensure that switches use the 802.3t standard costs instead of the legacy costs.
C. Mар different VLANs to different instances, and make a different switch root bridge in each instance.
D. Configure every switch with a unique bridge priority in the IST, which is also called spanning tree instance 0.
Answer: C

NEW QUESTION: 2
An architect proposes an Aruba solution with a hardware Mobility Master (MM) to a customer. The customer
has a disaster recovery site which is connected to the main site at Layer 3. The customer requires the MM to
remain available in case of a total site failure.
Which plan meets the customer requirements?
A. Deploy the virtual MM (VMM) to the disaster recovery site and set the VMM IP address to the same
address as the hardware MM.
B. Deploy a hardware MM to the disaster recovery site and set its IP address as the standby master for
MCs.
C. Deploy a hardware MM to the disaster recovery site and set up VRRP between them.
D. Deploy the virtual MM (VMM) to the disaster recovery site and configure clustering on the hardware
and software appliances.
Answer: C

NEW QUESTION: 3
Which statement about STP operations is true?
A. One designated port is defined per segment.
B. Non-designated ports are set in the forwarding state.
C. One root port exists per broadcast domain.
D. One root bridge exists per collision domain.
E. One root port is established per root bridge.
Answer: A

NEW QUESTION: 4
CPUベースのコンピューティングクラスターとAzureKubernetes Services（AKS）推論クラスターを含むAzure MachineLearningワークスペースがあります。分類モデルの作成に使用する予定のデータを含む表形式のデータセットを作成します。
Azure Machine Learningデザイナーを使用して、クライアントアプリケーションが新しいデータを送信し、応答として即時予測を取得することで分類モデルを利用できるWebサービスを作成する必要があります。
どの3つのアクションを順番に実行する必要がありますか？回答するには、適切なアクションをアクションのリストから回答領域に移動し、正しい順序で配置します。

Answer:
Explanation:

Explanation:
Step 1: Create and start a Compute Instance
To train and deploy models using Azure Machine Learning designer, you need compute on which to run the training process, test the model, and host the model in a deployed service.
There are four kinds of compute resource you can create:
Compute Instances: Development workstations that data scientists can use to work with data and models.
Compute Clusters: Scalable clusters of virtual machines for on-demand processing of experiment code.
Inference Clusters: Deployment targets for predictive services that use your trained models.
Attached Compute: Links to existing Azure compute resources, such as Virtual Machines or Azure Databricks clusters.
Step 2: Create and run a training pipeline..
After you've used data transformations to prepare the data, you can use it to train a machine learning model. Create and run a training pipeline Step 3: Create and run a real-time inference pipeline After creating and running a pipeline to train the model, you need a second pipeline that performs the same data transformations for new data, and then uses the trained model to inference (in other words, predict) label values based on its features. This pipeline will form the basis for a predictive service that you can publish for applications to use.
Reference:
https://docs.microsoft.com/en-us/learn/modules/create-classification-model-azure-machine-learning-designer/