Objective 2.4
Cybersecurity Defense ArchitectIdentify strategies to monitor an environment that requires nonstandard or out-of-band instrumentation and sensors, e.g. legacy data sources, OT/IC infrastructure environments
Objective 2.4 sits in Security Data Management, which carries 20% of the Cybersecurity Defense Architect exam. The questions below are original, written from the official objective title above, and each explanation cites the Splunk page it rests on.
Objective title verbatim from the official objectives. Splunk exam page ↗
A worked example
Shown solved, with the whole explanation open: this is what every question here carries.
An engineer must collect from a legacy device that offers only an API. Which Splunk input does the documentation name?
Correct.
Checked against help.splunk.com, August 2026Concept
Where a system was never designed to ship logs, the collector has to go and fetch them. A script turns any interface into an input without changing the device.
Why A
Splunk documents that you can use scripted input to get data from APIs, other remote data interfaces and message queues.
Source
Splunk Docs: Get data from APIs and other remote data interfaces through scripted inputs, checked August 2026You can use scripted input to get data from APIs, other remote data interfaces, and message queues.
Now you: objective 2.4 questions
No account needed. The explanation opens when you answer.
Sample question 1 of 2
An engineer plans to collect from network devices that emit only SNMP. Which two SNMP data sources does Splunk name?
Sample question 2 of 2
An engineer designs collection from a device that can only send over the network. Which transport does Splunk recommend, and why?
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Read the sources
These are the official pages the questions above cite. Reading them is studying the objective from the primary source, which is what the explanations point you toward anyway.
More objectives in Security Data Management
- 2.1 Explain how to develop and implement integration strategies for data-driven security operations
- 2.2 Identify data sources critical to cybersecurity operations, such as event sources, identity directories, asset management systems, and vulnerability - assessments. This can include non-security data sources, eg. observability tools
- 2.3 Identify high value / high signal / high noise data sources (e.g. Windows process vs EDR process flow, or network/VPC flow vs packet capture) and how they support security operations use cases
- 2.5 Develop a data lifecycle management strategy, including retention, storage tiering, summarization, data residency, and access control
- 2.6 Describe the value of data normalization in order to support integration into cybersecurity defense programs, such as security monitoring and threat hunting, e.g. with CIM, CEF
- 2.7 Implement security analytics strategies beyond traditional SIEM such as advanced techniques like data science, machine learning, behavioral analysis, and AI
- 2.8 Explain how cybersecurity defense data architectures scale using technologies and capabilities such as data mesh, data lakes, message bus, message routing, and federated search