1Z0-1127-25 LangChain and AI Application Development Practice Question
A developer is using LangChain's ConversationBufferMemory to store chat history. They notice that after many turns, the prompt becomes too large and exceeds the model's context window. What is the BEST memory type to use for this scenario?
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
✓
ConversationSummaryMemory
ConversationSummaryMemory periodically summarizes the conversation, keeping the prompt size manageable. It retains the gist of the conversation while reducing token usage.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
ConversationEntityMemory
Why it's wrong here
Entity memory extracts entities but does not solve the prompt size issue.
- ✗
ConversationBufferMemory with a large max_token_limit
Why it's wrong here
Simply increasing the limit does not prevent exceeding the context window; it only raises the threshold.
- ✓
ConversationSummaryMemory
Why this is correct
Summary memory compresses history into summaries, keeping the prompt small.
- ✗
ConversationStringBufferMemory
Why it's wrong here
This is not a standard LangChain memory type.
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