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NEW QUESTION: 1
A VTL6900-I system, the backup server connected to it is the Linux operating system. The backup server and the VTL6900-I system are directly connected through the FC.
The server identifies the device information as shown below. After the backup server is configured as a backup client for the VTL6900-I system and the backup VTL system on the server virtual library process, which the following operation and description is correct:
(Multiple choice.)
A. Server-side identification has been detected with the library device command is cat / proc / scsi / scsi.
B. When configuring the backup client on the VTL side, you need to know that the WWPN of the FC port that connects the VTL to the VTL is available on the server in the sys /class / fc_host directory and subdirectory to the corresponding WWPN port.
C. As shown in the figure, of the devices identified on the server side, the device labeled 2 is the information for the scanned tape.
D. As shown in the figure, among the devices identified on the server side, the device labeled 1 is the scanned library.
Answer: A,B,D

NEW QUESTION: 2
contoso.comという名前のMicrosoft Azure Active Directory（Azure AD）テナントがあります。
同じファイル形式のApp1、App2、App3という名前の3つのアプリケーションがあります。
会社はWindows Information Protection（WIP）を使用しています。 WIPには次の構成があります。
Windows情報保護モード：サイレント
保護されたアプリ：App1
免除アプリ：App2
App1から、File1という名前のファイルを作成します。
構成の影響は何ですか？回答するには、回答エリアで適切なオプションを選択します。
注：それぞれの正しい選択には1ポイントの価値があります。

Answer:
Explanation:

Explanation

NEW QUESTION: 3
When formulating an investment policy for a client, which of the following falls under "investor objectives?"
A. time horizon
B. investable funds
C. return objectives
D. liquidity needs
E. proxy voting
F. none of these answers
Answer: C
Explanation:
Explanation/Reference:
Explanation:
Return objectives are considered under "investor objectives." Liquidity needs, time horizon and proxy voting are considered under "investor constraints."

NEW QUESTION: 4
You have an Azure Machine Learning workspace that contains a CPU-based compute cluster and an Azure Kubernetes Services (AKS) inference cluster. You create a tabular dataset containing data that you plan to use to create a classification model.
You need to use the Azure Machine Learning designer to create a web service through which client applications can consume the classification model by submitting new data and getting an immediate prediction as a response.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

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/