Splunk Enterprise Certified Admin Practice Test

Which of the following is a characteristic of distributed architecture in Splunk?

1. Inline parsing of data

2. Single point of failure

3. Supports scaling out across multiple nodes

4. Only operates in local environments

The correct answer is: Supports scaling out across multiple nodes

In Splunk, distributed architecture is designed to enhance scalability and performance by allowing the system to handle large volumes of data effectively. One of the defining characteristics of this architecture is its ability to support scaling out across multiple nodes. This means that instead of requiring a single machine to manage all the data and processing, Splunk can distribute workloads across various servers. This distribution helps in efficiently managing elastic data environments, improving search performance, and increasing overall system resilience. This characteristic enables organizations to grow their Splunk infrastructure seamlessly as data volumes increase or as additional processing power is needed. As new nodes can be added to the system, this not only improves throughput but also mitigates the risk of performance bottlenecks. In contrast, inline parsing of data while it is being ingested is a feature of how Splunk processes data but does not specifically relate to its distributed nature. A single point of failure indicates a lack of redundancy and is contrary to the advantages offered by distributed architecture, which includes failover capabilities. Lastly, operating only in local environments limits the flexibility and scalability that distributed architecture provides, as it is designed to function across various environments—both local and cloud-based. Thus, supporting scaling out across multiple nodes is a fundamental feature of Splunk's distributed architecture