Questions and Answers: 166
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NEW QUESTION: 1
展示をご覧ください。
A
FortiGateの背後にいるユーザーがhttp://www.addictinggames.com(Addicting Games)にアクセスしようとしています。この構成に基づいて、正しい説明はどれですか。
A. Addicting.Gamesは、FilterOverrides構成でブロックされています。
B. Addcting.Gamesは、Categories構成に基づいて許可されます。
C. Addicting.Gamesは、ApplicationOverrides構成に基づいて許可されます。
D. Addicting.Gamesは、FilterOverridesアクションがExemptに設定されている場合にのみ許可できます。
Answer: C
NEW QUESTION: 2
You are validating the configuration of an Azure Search indexer.
The service has been configured with an indexer that uses the Import Data option. The index is configured using options as shown in the Index Configuration exhibit. (Click the Index Configuration tab.)
You use an Azure table as the data source for the import operation. The table contains three records with item inventory data that matches the fields in the Storage data exhibit. These records were imported when the index was created. (Click the Storage Data tab.) When users search with no filter, all three records are displayed.
When users search for items by description, Search explorer returns no records. The Search Explorer exhibit shows the query and results for a test. In the test, a user is trying to search for all items in the table that have a description that contains the word bag. (Click the Search Explorer tab.)
You need to resolve the issue.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: Yes
The ItemDescription field in not searchable.
Box 2: No
The ItemDescription field in not searchable, but we would need to recreate the index.
Box 3: Yes
An indexer in Azure Search is a crawler that extracts searchable data and metadata from an external Azure data source and populates an index based on field-to-field mappings between the index and your data source. This approach is sometimes referred to as a 'pull model' because the service pulls data in without you having to write any code that adds data to an index.
Box 4: No
References:
https://docs.microsoft.com/en-us/azure/search/search-what-is-an-index
https://docs.microsoft.com/en-us/azure/search/search-indexer-overview
NEW QUESTION: 3
Which two transactions can be reversed? Each correct answer presents a complete solution.
A. Vendor payments
B. An unposted depreciation transaction
C. A sales order
D. Fixed asset depreciation adjustment
E. A transaction that has already been reversed
Answer: B,C
NEW QUESTION: 4
한 회사에는 각각 크기가 약 20 바이트 (예보 당 20 기가 바이트) 인 10 억 개의 고유 위치의 출력 해상도로 15 분마다 업데이트 되는 일기 예보를 생성하는 애플리케이션이 있습니다. 매시간 예측 데이터는 전 세계적으로 약 5 백만 번 (초당 1,400 개 요청) 액세스되며 기상 이벤트 중에는 최대 10 배 더 많이 액세스됩니다. 업데이트 할 때마다 예측 데이터를 덮어 씁니다. 현재 일기 예보 애플리케이션의 사용자는 쿼리에 대한 응답이 각 요청에 대해 2 초 이내에 반환 될 것으로 기대합니다.
요구되는 요청 속도와 응답 시간을 충족하는 디자인은 무엇입니까?
A. Amazon ES 클러스터에 예측 위치를 저장합니다. Amazon API Gateway 엔드 포인트를 대상으로하는 Amazon CloudFront 배포를 오리진으로 쿼리에 응답하는 AWS Lambda 함수와 함께 사용합니다. 캐시 제어 제한 시간을 15 분으로 설정하여 API Gateway 단계에서 API 캐싱을 활성화 합니다.
B. Amazon ES 클러스터에 예측 위치를 저장합니다. 오리진으로 쿼리에 응답하는 AWS Lambda 함수와 함께 API Gateway 엔드 포인트를 대상으로하는 Amazon CloudFront 배포를 사용합니다. 15 분 동안 엣지 위치에서 로컬로 데이터를 캐시하는 Amazon Lambda @ Edge 함수를 생성합니다.
C. Amazon EFS 볼륨에 예측 위치를 저장합니다. Amazon EFS 볼륨을 탑재 한 Amazon EC2 인스턴스의 Auto Scaling 집합의 Elastic Load Balancing 그룹을 대상으로하는 Amazon CloudFront 배포를 생성합니다. CloudFront 배포에서 15 분 동안 설정된 캐시 제어 제한 시간을 설정합니다.
D. 예측 위치를 Amazon S3에 개별 객체로 저장합니다. Auto Scaling EC2 인스턴스 집합의 Elastic Load Balancing 그룹을 대상으로하는 Amazon CloudFront 배포를 생성하여 S3 객체의 오리진을 쿼리합니다. CloudFront 배포에서 캐시 제어 제한 시간을 15 분으로 설정합니다.
Answer: B
Explanation:
https://aws.amazon.com/blogs/networking-and-content-delivery/lambdaedge-design-best-practices/