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NEW QUESTION: 1
A. Option D
B. Option C
C. Option B
D. Option A
Answer: B
Explanation:
Explanation
Business Model Canvas is to define the customer environment, business model, and motivators for change, whereas Seven Elements Framework helps in communicating and negotiating with stakeholders.
NEW QUESTION: 2
セキュリティエンジニアは、すべてのユーザーへのアクセスを拒否するAmazon S3バケットポリシーを作成します。数日後、セキュリティエンジニアはバケットポリシーにステートメントを追加して、他の1人の従業員に読み取り専用アクセスを許可します。ポリシーを更新した後も、従業員は引き続きアクセス拒否メッセージを受け取ります。このアクセス拒否の考えられる原因は何ですか？
A. バケットのACLを更新する必要があります。
B. バケットポリシーが有効になるまで数分かかります
C. IAMポリシーにより、ユーザーはバケットにアクセスできません
D. 許可により、拒否によりオーバーライドされます。
Answer: D
NEW QUESTION: 3
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/