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NEW QUESTION: 1
会社は有効なタイムソースに関心があり、NTP認証を設定するよう要求しました。複数のNTPソースがネットワーク上にあります。 NTPソースを認証および同期するには、クライアントデバイスでどの構成が必要ですか?
A. 階層ハッシュ
B. 証明書の事前共有キー
C. 信頼できるキー
D. SSL
Answer: C
NEW QUESTION: 2
You are developing a Docker/Go using Azure App Service Web App for Containers. You plan to run the container in an App Service on Linux. You identify a Docker container image to use.
None of your current resource groups reside in a location that supports Linux. You must minimize the number of resource groups. You need to create the application and perform an initial deployment required.
Which three Azure CLI commands should you use to develop the solution? To answer, move the appropriate commands from the list of commands to the answer area and arrange them in the correct order.
Answer:
Explanation:
Explanation
az group create
az appservice plan create
az webapp create
NEW QUESTION: 3
Your network environment includes a Microsoft Visual Studio Team Foundation Server 2012 (TFS) server. A single build machine is installed with one controller and two agents. The working directories are configured to use the following working directory:
$(SystemDrive)\Builds\$(BuildAgentId)\$(BuildDefinitionPath).
Your system drive is running out of space and some builds often fail with the following error:
"The specified path, file name, or both are too long. The fully qualified file name must be less than 260 characters, and the directory name must be less than 248 characters."
You need to meet the following requirements:
- Point the working path to drive D.
- Construct a working directory that can be used to define the shortest unique path for each build agent it is used on.
What should you do?
A. Create a system variable named BuildDrive and set its value to D: \.
B. Set the Build Agent working directory path to D: \BId\$(BuildAqentld)\$(BuildDetinitionPath).
C. Set the Build Service working directory path to D: \BId\$(BuildAqentld)\$(BuildDefinitionPath).
D. Set the Team Foundation Build variable named $(BuildDrive) to the value D: \.
Answer: B
NEW QUESTION: 4
제조 회사에 분류된 과거 판매 데이터 세트가 있는 경우 제조업체는 분기마다 특정 부품의 몇 개 단위를 생산해야 하는지 예측하려고 합니다.이 문제를 해결하기 위해 어떤 기계 학습 방법을 사용해야 합니까?
A. 선형 회귀
B. 랜덤 컷 포레스트 (RCF)
C. 주성분 분석 (PCA)
D. 로지스틱 회귀
Answer: A
Explanation:
https://docs.aws.amazon.com/zh_tw/machine-learning/latest/dg/regression-model-insights.html