- A
ConversationBufferWindowMemory; set a window size
Why wrong: WindowMemory keeps last k messages, not a summary.
- B
ConversationTokenBufferMemory; set a token limit
Why wrong: TokenBufferMemory truncates by token count, not summary.
- C
ConversationSummaryMemory; provide an LLM to generate summaries
SummaryMemory needs an LLM to compress the conversation into a summary.
- D
ConversationBufferMemory; no additional step
Why wrong: BufferMemory stores full history, not summaries.
Question 908 of 991
1Z0-1127 LangChain and AI Application Development Practice Question
This 1Z0-1127 practice question tests your understanding of langchain and ai application development. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A team is building a conversational chatbot using LangChain and OCI Generative AI. They want to maintain a summary of the conversation rather than storing the entire history, to keep within token limits. Which memory class should they use, and what additional step is required when initializing the 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
✓
ConversationSummaryMemory; provide an LLM to generate summaries
ConversationSummaryMemory is designed to maintain a running summary of the conversation instead of storing the full history, which directly addresses the requirement to stay within token limits. The additional step required is providing an LLM (e.g., via `llm=ChatOpenAI(...)`) because the memory class uses the LLM to generate and update the summary dynamically.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
ConversationBufferWindowMemory; set a window size
Why it's wrong here
WindowMemory keeps last k messages, not a summary.
- ✗
ConversationTokenBufferMemory; set a token limit
Why it's wrong here
TokenBufferMemory truncates by token count, not summary.
- ✓
ConversationSummaryMemory; provide an LLM to generate summaries
Why this is correct
SummaryMemory needs an LLM to compress the conversation into a summary.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
ConversationBufferMemory; no additional step
Why it's wrong here
BufferMemory stores full history, not summaries.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the distinction between memory classes that truncate versus those that summarize, and the trap here is that candidates may confuse ConversationTokenBufferMemory (which drops messages) with summary-based memory, missing the critical requirement to provide an LLM for summary generation.
Detailed technical explanation
How to think about this question
Under the hood, ConversationSummaryMemory uses the provided LLM to call a summarization chain each time a new message is added, compressing the entire conversation into a concise summary that is stored in memory. This approach is token-efficient but introduces latency and cost due to the LLM call per interaction, and it can lose nuanced details that a raw history would preserve. In a real-world scenario, this is ideal for long-running sessions where token budgets are tight, but developers must ensure the LLM used for summarization is fast and cost-effective.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A practitioner preparing for the 1Z0-1127 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this 1Z0-1127 question test?
LangChain and AI Application Development — This question tests LangChain and AI Application Development — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: ConversationSummaryMemory; provide an LLM to generate summaries — ConversationSummaryMemory is designed to maintain a running summary of the conversation instead of storing the full history, which directly addresses the requirement to stay within token limits. The additional step required is providing an LLM (e.g., via `llm=ChatOpenAI(...)`) because the memory class uses the LLM to generate and update the summary dynamically.
What should I do if I get this 1Z0-1127 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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