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NEW QUESTION: 1
Drag and Drop Question
You are performing the initial configuration on a new Cisco device. Drag the task from the left onto the required or optional category on the right.
Answer:
Explanation:
NEW QUESTION: 2
A company with a big information technology (IT) department has hired a lead business analyst (BA) to enhance its business analysis practices. The lead BA discovers that sponsors are not satisfied with project outcomes. Developers complain about incomplete, ambiguous, and changing requirements. All stakeholders, including project managers, are blaming long cycles of analysis for the delays. The business analysts, in turn, feel overwhelmed with the number of projects and frustrated by the lack of collaboration from reviewers of their deliverables. All of the evidence is anecdotal and none of the groups could strongly substantiate their opinions.
The lead BA wants the BAs to report anticipated and actual completion dates for their deliverables, as well as the time spent on planned and unplanned activities. What is the lead BA trying to do by analyzing the results of such measurements?
A. Establish an equitable compensation system
B. Motivate the BA to work belter
C. Ensure even workloads of the BA
D. Identify typical missing or wasteful activities
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/
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