1Z0-1127-25 LangChain and AI Application Development Practice Question
A team is building a LangChain agent that needs to answer questions using both a company-internal knowledge base (stored in Oracle AI Vector Search) and live web search. Which THREE components should they include in the agent setup?
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
✓
AgentExecutor to manage the ReAct loop
A LangChain agent requires a set of tools (vector store retriever as a tool, web search tool), and an AgentExecutor to orchestrate the reasoning and tool calls. The LLM is already part of the agent definition.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
AgentExecutor to manage the ReAct loop
Why this is correct
AgentExecutor runs the agent's reasoning and tool execution loop.
- ✓
A tool wrapping the vector store retriever
Why this is correct
The retriever must be exposed as a tool so the agent can query the knowledge base.
- ✗
ConversationBufferMemory for storing chat history
Why it's wrong here
Memory is optional and not a core component of the agent setup; it can be added separately.
- ✗
A custom tool for logging agent steps
Why it's wrong here
Logging is useful but not a required component for the agent to function.
- ✓
A tool wrapping a web search engine (e.g., Tavily, SerpAPI)
Why this is correct
Live web search requires a dedicated tool.
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