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NEW QUESTION: 1
Answer:
Explanation:
Explanation
Box 1: Number of namespaces: Four
Box 2: Number of Dags: Two
Depending on your architecture and infrastructure you have two choices:
These choices are tied to the DAG architecture.
-Deploy a unified namespace for the site resilient datacenter pair (unbound model).
-In an unbound model, you have a single DAG deployed across the datacenter pair.
-Deploy a dedicated namespace for each datacenter in the site resilient pair (bound model).
In a bound model, multiple namespaces are preferred, two per datacenter (primary and failback namespaces), to prevent clients trying to connect to the datacenter where they may have no connectivity.
As its name implies, in a bound model, users are associated (or bound) to a specific datacenter. In other words, there is preference to have the users operate out of one datacenter during normal operations and only have the users operate out of the second datacenter during failure events. There is also a possibility that users do not have equal connectivity to both datacenters. Typically, in a bound model, there are two DAGs deployed in the datacenter pair. Each DAG contains a set of mailbox databases for a particular datacenter; by controlling where the databases are mounted, you control connectivity.
From scenario:
The servers in the New York and London offices are members of a database availability group (DAG).
Fabrikam identifies the following high-availability requirements for the planned deployment:
Mailbox databases that contain mailboxes for the New York office users must only be activated on the servers in the London office manually.
All client access connections to the London and New York offices must use load-balanced namespaces. The load balancing mechanism must perform health checks.
References:https://blogs.technet.microsoft.com/exchange/2015/10/06/namespace-planning-in-exchange-2016/
NEW QUESTION: 2
A. Option D
B. Option E
C. Option C
D. Option B
E. Option A
Answer: B
NEW QUESTION: 3
この質問については、TerramEarthのケーススタディを参照してください。
TerramEarthのCTOは、接続された車両の生データを使用して、開発チームの車両が障害に集中する時期を特定するのに役立てたいと考えています。アナリストが車両データを一元的に照会できるようにする必要があります。どのアーキテクチャをお勧めしますか？
A）
B）
C）
D）
A. オプションD
B. オプションC
C. オプションB
D. オプションA
Answer: D
Explanation:
説明
https://cloud.google.com/solutions/iot/
https://cloud.google.com/solutions/designing-connected-vehicle-platform
https://cloud.google.com/solutions/designing-connected-vehicle-platform#data_ingestion
http://www.eweek.com/big-data-and-analytics/google-touts-value-of-cloud-iot-core-for-analyzing-connected-car
https://cloud.google.com/solutions/iot/
プッシュエンドポイントはロードバランサーにすることができます。
コンテナクラスタを使用できます。
Stream AnalyticsのCloud Pub / Sub
参照：https://cloud.google.com/pubsub/
https://cloud.google.com/solutions/iot/
https://cloud.google.com/solutions/designing-connected-vehicle-platform
https://cloud.google.com/solutions/designing-connected-vehicle-platform#data_ingestion
http://www.eweek.com/big-data-and-analytics/google-touts-value-of-cloud-iot-core-for-analyzing-connected-car
https://cloud.google.com/solutions/iot/
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