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NEW QUESTION: 1
As a network administrator, if you are experiencing intermittent security issues what is the first thing you should do?
A. Consider alternative solutions
B. Define a solution
C. Try obvious fixes
D. Isolate the problem
Answer: D
Explanation:
Intermittent problems require isolation before any solution can be found. It is important to limit possible causes. This is done by isolating the network segment. By slowly removing each possible culprit you eventually will have isolated the actual problem.
Answer option B is incorrect. You cannot define a solution if you have not isolated the problem. Answer option A is incorrect. Once you have isolated the problem, you can then attempt obvious fixes.
Answer option D is incorrect. Alternative solutions should be considered after you have isolated the problem and tried obvious fixes.
NEW QUESTION: 2
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: 3
An approval policy can be applied to which two options? (Choose two.)
A. An event
B. A catalog user
C. A catalog service
D. An action
Answer: C,D
Explanation:
Explanation/Reference:
Reference https://pubs.vmware.com/vra-70/index.jsp?topic=%2Fcom.vmware.vrealize.automation.doc%
2FGUID-4463B302-4365-4374-8954-7B582D34D41A.html
NEW QUESTION: 4
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You have a server named Server1 that runs Windows Server 2016.
Server1 hosts a line-of-business application named App1. App1 has a memory leak that occasionally causes the application to consume an excessive amount of memory.
You need to log an event in the Application event log whenever App1 consumes more than 4 GB of memory.
Solution: You create a system configuration information data collector.
Does this meet the goal?
A. No
B. Yes
Answer: A
Explanation:
Explanation/Reference:
Explanation: