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NEW QUESTION: 1
Scenario: A Citrix Architect is designing a XenApp and XenDesktop environment and needs to design machine catalogs and Delivery Groups. Users should only be able to access their assigned resources.
Click on the Exhibit button to view the design requirements.

What is the appropriate design using the minimum number of machine catalogs and Delivery Groups, based on the requirements in the Exhibit?
A. 1 machine catalog, 2 Delivery Groups, and no tagging
B. 3 machine catalogs, 4 Delivery Groups, and no tagging
C. 1 machine catalog, 1 Delivery Group, and tagging for the machine hosting the Engineering application - QA version
D. 2 machine catalogs, 3 Delivery Groups, and tagging for the machine hosting the Engineering application
- QA version
Answer: A

NEW QUESTION: 2
참 또는 거짓 : "Amazon ElastiCache와 관련하여 애플리케이션의 관점에서 클러스터 구성 엔드 포인트에 연결하는 것은 개별 캐시 노드에 직접 연결하는 것과 다릅니다."
A. 응용 프로그램의 관점에서 보면 클러스터 구성 끝점에 연결하는 것이 고유 한 노드 식별자를 가지고 있기 때문에 개별 캐시 노드에 직접 연결하는 것과 다르지 않습니다.
B. 응용 프로그램의 관점에서 볼 때 클러스터 구성 끝점에 연결하는 것은 개별 캐시 노드에 직접 연결하는 것과 다르지 않습니다.
C. False이면 클러스터 구성 끝점에 연결할 수 있지만 캐시 노드에는 연결할 수 없습니다.
D. False이면 캐시 노드에 연결할 수 있지만 클러스터 구성 끝점에는 연결할 수 없습니다.
Answer: B
Explanation:
설명
이것은 사실입니다. 애플리케이션의 관점에서 볼 때, 클러스터 구성 엔드 포인트에 연결하는 것은 개별 캐시 노드에 직접 연결하는 것과 다를 바 없습니다. 캐시 노드에 연결하는 과정에서 응용 프로그램은 구성 끝점의 DNS 이름을 확인합니다. 구성 끝점은 모든 캐시 노드에 대한 CNAME 항목을 유지 관리하므로 DNS 이름은 노드 중 하나를 확인합니다. 클라이언트는 그 노드에 연결할 수 있습니다.
http://docs.aws.amazon.com/AmazonElastiCache/latest/UserGuide/AutoDiscovery.HowAutoDiscoveryWorks.ht

NEW QUESTION: 3
Universal Containers (UC) uses Apache Kafka on Heroku to stream shipment inventory data in real time throughout the world. A Kafka topic is used to send messages with updates on the shipping container GPS coordinates as they are in transit. UC is using a Heroku Kafka basic-0 plan. The topic was provisioned with 8 partitions, 1 week of retention, and no compaction. The keys for the events are being assigned by Heroku Kafka, which means that they will be randomly distributed between the partitions.
UC has a single-dyno consumer application that persists the data to their Enterprise Data Warehouse (EDW).
Recently, they've been noticing data loss in the EDW.
What should an Architect with Kafka experience recommend?
A. Use Heroku Redis to store message receipt information to account for "at-least" once delivery, which will guarantee that messages are never processed more than once. Scale up the consumer dynos to match the number of partitions so that there is one process for each partition.
B. Upgrade to a larger Apache Kafka for Heroku plan, which has greater data capacity.
C. Enable compaction on the topic to drop older messages, which will drop older messages with the same key.
Answer: A

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