Question 13
Domain 3: Knowledge Integration, Data Handling, Cognition, Planning, and MemoryYou are designing a pipeline for a conversational agent that answers questions about IT incidents using real-time logs (unstructured) and CMDB (structured) data. What design best supports low-latency reasoning across both sources?
Correct answer: D
Explanation
Indexing both sources in a unified vector database supports “hybrid semantic search,” letting the agent retrieve relevant log events and CMDB records through one low-latency query path. This design avoids separate lookups and enables fast reasoning across unstructured and structured data in the same retrieval layer.
Why each option is right or wrong
A. Periodically generate plain-text summaries of recent logs and store them in a CSV file that the agent reads on each query.
B. Rely only on CMDB data to avoid inconsistencies in log formats and time.
C. Use regex-based pattern matching on logs and ignore CMDB data, since structured asset metadata is rarely needed for incident triage.
D. Index both logs and CMDB in a unified vector database for hybrid semantic search
A unified vector database is the only option here that gives a single retrieval path for both the unstructured incident logs and the structured CMDB records, which is what keeps latency low during agent reasoning. By embedding both data types and querying them together with hybrid semantic search, the system avoids two separate fetch-and-join steps, reducing round trips and enabling the model to correlate log events with configuration context in one pass.