1Z0-1127-25 LangChain and AI Application Development Practice Question
A company uses LangChain with OCI Generative AI. They notice that their agent-based application occasionally exceeds the rate limits of the OCI Generative AI service, causing errors. Which strategy is MOST effective for handling rate limits in a production LangChain application?
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 retry mechanism with exponential backoff when calling the model
Using a retry mechanism with exponential backoff is a standard and effective approach for handling rate limits.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Implement a retry mechanism with exponential backoff when calling the model
Why this is correct
Retry with exponential backoff is the standard approach to handle rate limiting errors gracefully.
- ✗
Increase the k value in the retriever to reduce the number of API calls
Why it's wrong here
k affects the number of documents retrieved, not the number of model calls.
- ✗
Switch to a smaller model to reduce token consumption
Why it's wrong here
Smaller models may not solve rate limit issues if the number of requests is high.
- ✗
Reduce the chunk_size parameter in text splitters
Why it's wrong here
Chunk size affects embedding and retrieval, not API call rate limits.
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