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LangChain and AI Application DevelopmentmediumMultiple ChoiceObjective-mapped

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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