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NEW QUESTION: 1
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/
NEW QUESTION: 2
A. Option B
B. Option D
C. Option A
D. Option C
Answer: D
NEW QUESTION: 3
Which statement is true about Remote Triggered Black Hole Filtering feature (RTBH)?
A. In FTBH filtering the trigger device is always an ISP edge router
B. The Null0 interface used for filtering is able to receive the traffic, but never forwards it
C. It drops malicious traffic at the customer edge router by forwarding it to a Null0 interface
D. It helps mitigate DDoS attack based only on source address
E. In RTBH filtering the trigger device redistributes static route to the iBGP peers
F. It works in conjunction with QoS to drop the traffic that has less priority
Answer: E