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NEW QUESTION: 1
회사에는 많은 AWS 계정이 정의되어 있으며 모두 AWS Organizations를 통해 관리됩니다. 하나의 AWS 계정에는 중요한 데이터가 포함 된 S3 버킷이 있습니다. AWS 조직의 모든 사용자가이 버킷에 액세스 할 수 있도록 하려면 어떻게해야 합니까?
선택 해주세요:
A. 버킷 정책에 aws : AccountNumber와 관련된 조건이 있는지 확인하십시오.
B. 버킷 정책에 aws : PrincipalOrglD와 관련된 조건이 있는지 확인하십시오.
C. 버킷 정책에 aws : PrincipaliD와 관련된 조건이 있는지 확인하십시오.
D. 버킷 정책에 aws : OrglD와 관련된 조건이 있는지 확인하십시오.
Answer: B
Explanation:
설명
AWS 설명서에는 다음이 언급되어 있습니다.
이제 AWS Identity and Access Management (1AM)를 사용하면 1AM 주체 (사용자 및 역할)의 AWS 조직을 사용하여 AWS 리소스에 대한 액세스를보다 쉽게 제어 할 수 있습니다. 일부 서비스의 경우 리소스 기반 정책을 사용하여 리소스에 액세스 할 수 있는 계정 및 보안 주체와 리소스에서 수행 할 수있는 작업을 지정하는 권한을 부여합니다. 버킷 정책의 조건이 aws를 언급해야 하므로 이제 리소스에 액세스하는 모든 주체가 조직 옵션 BC 및 D의 계정에서 계정을 사용하도록 요구하기 위해 이러한 정책에서 새로운 조건 키 aws : PrincipalOrglD를 사용할 수 있습니다. PrincipalOrglD 조직을 통한 액세스 제어에 대한 자세한 내용은 아래 링크를 참조하십시오.
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NEW QUESTION: 2


Answer:
Explanation:





https://www.henkboelman.com/azure-search-the-basics/

NEW QUESTION: 3
Northern Trail Outfitter's Quality Assurance process requires that a team of users evaluates all parameters of an email send, including Email, Subject Line, Send Classification, and other properties, prior to manually sending the email.
What tool in Email Studio enables a marketer to save and execute a send manually?
A. User-Initiated Email
B. Guided Send
C. Send Activity
D. Send Flow
Answer: B

NEW QUESTION: 4
会社の工場でTwitterハッシュタグを監視します。次に、関連する感情を含む接続された人々と場所のグラフを作成します。
監視されるハッシュタグは複数の言語を使用しますが、グラフは英語で表示されます。
必要なAzure Cognitive Servicesエンドポイント（または計画されたグラフ）を推奨する必要があります。どのCognitive Servicesエンドポイントを推奨しますか？
A. 言語検出。感情分析、およびキーフレーズ抽出
B. 翻訳テキスト、コンテンツモデレーター、キーフレーズ抽出
C. 翻訳テキスト。感情分析、および名前付きエンティティの認識
D. 言語検出。コンテンツモデレーター、およびキーフレーズ抽出
Answer: A
Explanation:
Explanation
Sentiment analysis, which is also called opinion mining, uses social media analytics tools to determine attitudes toward a product or idea.
Translator Text: Translate text in real time across more than 60 languages, powered by the latest innovations in machine translation.
The Key Phrase Extraction skill evaluates unstructured text, and for each record, returns a list of key phrases.
This skill uses the machine learning models provided by Text Analytics in Cognitive Services.
This capability is useful if you need to quickly identify the main talking points in the record. For example, given input text "The food was delicious and there were wonderful staff", the service returns "food" and
"wonderful staff".
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/how-tos/text-analytics-how-to-entity-lin
https://docs.microsoft.com/en-us/azure/search/cognitive-search-skill-keyphrases