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Implementing AI SolutionshardMultiple ChoiceObjective-mapped

AI0-001 Implementing AI Solutions Practice Question

An AI developer is building an agent that can book flights and hotels by calling external APIs. The agent needs to decide which API to call and in what order based on user requests. Which pattern is BEST suited for this multi-step reasoning and tool use?

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

Apply the ReAct pattern (Reasoning and Acting)

The ReAct pattern (Reasoning + Acting) combines chain-of-thought reasoning with tool use, allowing the agent to plan, call APIs, and incorporate results. Function calling alone doesn't provide multi-step reasoning. Simple RAG is for retrieval, not action. Fine-tuning doesn't give dynamic tool selection.

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 simple Retrieval-Augmented Generation (RAG) pipeline

    Why it's wrong here

    RAG is for retrieving information from a knowledge base, not for taking actions like calling APIs.

  • Fine-tune a model to output API call sequences directly

    Why it's wrong here

    Fine-tuning would require labeled data for many scenarios and lacks the flexibility to handle novel user requests; the model would not be able to reason about new situations.

  • Use function calling with a fixed sequence of API calls

    Why it's wrong here

    Function calling allows the model to invoke functions but without multi-step reasoning; a fixed sequence lacks flexibility.

  • Apply the ReAct pattern (Reasoning and Acting)

    Why this is correct

    ReAct interleaves reasoning steps (thinking about what to do next) with actions (calling APIs), enabling dynamic multi-step workflows.

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