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LangChain and AI Application DevelopmenthardMultiple ChoiceObjective-mapped

1Z0-1127-25 LangChain and AI Application Development Practice Question

A company is deploying a LangChain agent that uses a custom tool to query an external API. The agent must handle rate limits gracefully. Which approach should the developer 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

Use the Tool's built-in RateLimiter wrapper with a specified max_requests_per_second

LangChain provides a built-in RateLimiter tool wrapper that can be applied to any tool to enforce rate limits. Alternatively, custom retry logic can be added, but using the built-in wrapper is the recommended pattern.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • Increase the tool's timeout to reduce request frequency

    Why it's wrong here

    Timeout controls how long to wait for a response, not the request rate.

  • Deploy multiple agent instances to distribute requests

    Why it's wrong here

    Multiple instances would increase total request rate, worsening the problem.

  • Ignore rate limits and rely on the LLM to slow down

    Why it's wrong here

    LLMs do not inherently respect API rate limits; explicit handling is needed.

  • Use the Tool's built-in RateLimiter wrapper with a specified max_requests_per_second

    Why this is correct

    The RateLimiter wrapper automatically throttles calls to the tool.

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