Topic 1 Question 946
A company runs its production workload on an Amazon Aurora MySQL DB cluster that includes six Aurora Replicas. The company wants near-real-time reporting queries from one of its departments to be automatically distributed across three of the Aurora Replicas. Those three replicas have a different compute and memory specification from the rest of the DB cluster.
Which solution meets these requirements?
Create and use a custom endpoint for the workload
Create a three-node cluster clone and use the reader endpoint
Use any of the instance endpoints for the selected three nodes
Use the reader endpoint to automatically distribute the read-only workload
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コメント(4)
- 正解だと思う選択肢: A
Custom Endpoints:
Custom endpoints in Amazon Aurora allow you to group specific replicas together and route traffic only to those replicas. This is particularly useful when you have replicas with different compute and memory specifications and want to direct specific workloads, such as reporting queries, to those replicas. By creating a custom endpoint, you can include the three specific Aurora Replicas that have the required compute and memory configurations, ensuring that your near-real-time reporting queries are automatically distributed among these replicas.
👍 6[Removed]2024/08/17 - 正解だと思う選択肢: A
A - You can assign the three replicas with different compute and memory specifications to the custom endpoint, ensuring that queries are distributed only among these replicas. B - Expensive, cumbersome, and unnecessary. The clone has separate storage and compute resources. C - You can't target just the three replicas you want. D - The reader endpoint automatically distributes read queries across ALL Aurora Replicas in the cluster.
👍 2LeonSauveterre2025/01/04 - 正解だと思う選択肢: A
This is why I choose Option A:
Using a Custom endpoint: This allows you to specify a specific set of Aurora replicas be used for the reporting queries, enabling you to target the three replicas with different compute and memory specifications while distributing the workload across them.
👍 1JA20182024/12/02
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