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NEW QUESTION: 1
When attempting to place a datastore in Maintenance Mode, the task remains at 1%.
What are two potential causes for this? (Choose two.)
A. Storage DRS datastores have insufficient space to accommodate failover.
B. Storage DRS is disabled on one or more virtual machine(s) disk(s).
C. Storage DRS provisioning network has been limited with I/O control policies.
D. Storage DRS rules prevent migration recommendations for the disk.
Answer: B,D
Explanation:
Explanation/Reference:
Explanation:
You place a datastore in maintenance mode when you must take it out of usage to service it. A datastore enters or leaves maintenance mode only as a result of a user request.
Reference: https://pubs.vmware.com/vsphere-51/index.jsp?topic=%
2Fcom.vmware.vsphere.troubleshooting.doc%2FGUID-91D4FBBC-0677-44DA-B0F2-
43A3504F8CA0.html

NEW QUESTION: 2
회사는 약 20 바이트 크기 (예측 당 20 기가 바이트)의 10 억 개의 고유 한 위치의 출력 해상도로 15 분마다 업데이트되는 일기 예보를 생성하는 응용 프로그램을 보유하고 있습니다. 1 시간마다 예측 데이터는 전 세계적으로 약 5 백만 번 (초당 1,400 건의 요청) 및 최대 10 배까지 액세스됩니다. 예측 데이터는 업데이트 할 때마다 덮어 씁니다. 현재 일기 예보 응용 프로그램 사용자는 각 요청에 대해 2 초 이내에 쿼리에 대한 응답이 반환 될 것으로 예상합니다.
어떤 디자인이 필요한 요청 속도와 응답 시간을 충족합니까?
A. Amazon ES 클러스터에 예측 위치를 저장합니다. 쿼리에 원본으로 응답하는 AWS Lambda 함수와 함께 Amazon API Gateway 엔드 포인트를 대상으로하는 Amazon CloudFront 배포를 사용하십시오.
캐시 제어 시간 종료가 15 분으로 설정된 상태에서 API 게이트웨이 스테이지에서 API 캐싱을 사용하십시오.
B. Amazon ES 클러스터에 예측 위치를 저장합니다. 쿼리에 원본으로 응답하는 AWS Lambda 함수가 포함 된 API Gateway 엔드 포인트를 대상으로하는 Amazon CloudFront 배포를 사용하십시오. 데이터를 엣지 로케이션에서 15 분 동안 로컬로 캐시하는 Amazon Lambda @ Edge 함수를 생성하십시오.
C. Amazon EFS 볼륨에 예측 위치를 저장합니다. Amazon EFS 볼륨을 마운트 한 Amazon EC2 인스턴스의 Auto Scaling 집합의 Elastic Load Balancing 그룹을 대상으로하는 Amazon CloudFront 배포를 생성합니다. CloudFront 배포에서 설정된 캐시 제어 시간 초과를 15 분 동안 설정하십시오.
D. Amazon S3의 예측 위치를 개별 객체로 저장합니다. EC3 인스턴스의 Auto Scaling 집합의 Elastic Load Balancing 그룹을 대상으로 Amazon CloudFront 배포를 생성하고 S3 객체의 출처를 쿼리합니다. CloudFront 배포에서 캐시 제어 시간 초과를 15 분 동안 설정하십시오.
Answer: B

NEW QUESTION: 3
You are designing a serving layer for data. The design must meet the following requirements:
Authenticate users by using Azure Active Directory (Azure AD).
Serve as a hot path for data.
Support query scale out.
Support SQL queries.
What should you include in the design?
A. Azure Blob storage
B. Azure Synapse Analytics
C. Azure Data Lake Storage
D. Azure Cosmos DB
Answer: D
Explanation:
Explanation
Do you need serving storage that can serve as a hot path for your data? If yes, narrow your options to those that are optimized for a speed serving layer. This would be Cosmos DB among the options given in this question.
Note: Analytical data stores that support querying of both hot-path and cold-path data are collectively referred to as the serving layer, or data serving storage.
There are several options for data serving storage in Azure, depending on your needs:
* Azure Synapse Analytics
* Azure Cosmos DB
* Azure Data Explorer
* Azure SQL Database
* SQL Server in Azure VM
* HBase/Phoenix on HDInsight
* Hive LLAP on HDInsight
* Azure Analysis Services
Reference:
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/analytical-data-stores