1Z0-1127-25 LangChain and AI Application Development Practice Question
A company is deploying a LangChain application using OCI Generative AI. They need to comply with a policy that requires all prompts sent to the LLM to be logged for audit, and they must also handle rate limits gracefully. Which TWO strategies should they implement?
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
✓
Implement a custom LangChain callback that logs the prompt before sending it to the model
Using LangChain callbacks (e.g., on_llm_start) allows capturing prompts for logging without modifying the chain. For rate limits, adding a retry with exponential backoff (e.g., via tenacity or a custom callback) ensures resilience without dropping requests.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use a faster LLM to reduce response time
Why it's wrong here
A faster LLM does not address logging or rate limit handling.
- ✓
Implement a custom LangChain callback that logs the prompt before sending it to the model
Why this is correct
Callbacks are the idiomatic way to intercept and log prompts in LangChain.
- ✗
Increase the batch size of requests to reduce the number of API calls
Why it's wrong here
Batch size may hit rate limits faster; it does not help with graceful handling.
- ✓
Wrap the LLM call in a retry mechanism with exponential backoff to handle rate limit errors
Why this is correct
Exponential backoff prevents overwhelming the API and recovers gracefully from rate limits.
- ✗
Store the full conversation history in the prompt's system message
Why it's wrong here
This does not log prompts for audit; it only includes history.
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