1Z0-1127-25 LangChain and AI Application Development Practice Question
A team is implementing a conversational chatbot that needs to remember a user's previous messages within the same session. They are using LangChain with OCI Generative AI. Which memory type and persistence approach should they choose for session-only memory?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
ConversationBufferMemory stored in-memory
ConversationBufferMemory stores the full message history in memory, suitable for short sessions. For session-only memory, in-memory persistence is sufficient; no external database is needed. Summary memory is more suited for long conversations where token limits are a concern.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
ConversationBufferMemory persisted in a vector store
Why it's wrong here
Vector stores are for embeddings, not for storing raw conversation history; this adds complexity without benefit.
- ✗
ConversationSummaryMemory stored in a local file
Why it's wrong here
Summary memory compresses history, which may lose details needed for accurate responses; file persistence is unnecessary for session-only memory.
- ✓
ConversationBufferMemory stored in-memory
Why this is correct
Buffer memory retains the full conversation history in Python memory, ideal for session-scoped chatbots that do not require long-term persistence.
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ConversationSummaryMemory persisted in Oracle Database
Why it's wrong here
Summary memory condenses history, which is overkill for short sessions; persisting in a database adds unnecessary latency.
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