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15 AI Agents14 Post Quiz

AI Agents - Post-Quiz

Test your knowledge after completing the AI Agents phase.

Instructions

  • Answer all 10 questions
  • Try to answer without referring to materials
  • Time limit: 20 minutes
  • Compare your score with the pre-quiz!

Questions

Question 1

Which tool schema property tells the LLM WHEN to use a function?

  • A) name
  • B) description
  • C) parameters
  • D) required

Answer

B) description

Explanation: The description field explains what the function does and when it should be used, helping the LLM make intelligent tool selection decisions.

Question 2

In the ReAct pattern, what comes after an Action?

  • A) Final Answer
  • B) Another Action
  • C) Observation
  • D) Thought

Answer

C) Observation

Explanation: The ReAct loop is: Thought → Action → Observation → (repeat) → Final Answer. Observations provide results of actions.

Question 3

What is the purpose of the max_iterations parameter in agent loops?

  • A) To make the agent faster
  • B) To prevent infinite loops
  • C) To improve accuracy
  • D) To reduce costs

Answer

B) To prevent infinite loops

Explanation: max_iterations caps the number of agent steps, preventing the agent from looping forever if it can’t reach a final answer.

Question 4

Which validation is most critical for a date input parameter?

  • A) Check if it’s a string
  • B) Verify the format (YYYY-MM-DD) and logical validity
  • C) Make sure it’s not empty
  • D) Convert to uppercase

Answer

B) Verify the format (YYYY-MM-DD) and logical validity

Explanation: Dates need format validation (correct pattern) AND logic validation (not in past if required, start < end, etc.).

Question 5

What’s the main advantage of using enum in parameter schemas?

  • A) Faster execution
  • B) Limits choices to valid options
  • C) Reduces token usage
  • D) Improves accuracy

Answer

B) Limits choices to valid options

Explanation: Enums restrict the LLM to a predefined set of valid values, preventing invalid inputs.

Question 6

In a multi-agent system, what role does a “Planner” agent typically have?

  • A) Executing all tasks
  • B) Breaking down complex tasks into steps
  • C) Reviewing outputs for quality
  • D) Managing API calls

Answer

B) Breaking down complex tasks into steps

Explanation: Planner agents analyze the overall task and create a step-by-step plan for other agents to execute.

Question 7

What is “short-term memory” in agent systems?

  • A) The model’s training data
  • B) Recent conversation history
  • C) Cached API responses
  • D) User profile data

Answer

B) Recent conversation history

Explanation: Short-term memory stores the current conversation context (recent messages) for immediate reference.

Question 8

Why use exponential backoff in retry logic?

  • A) To fail faster
  • B) To give services time to recover between retries
  • C) To save money
  • D) To improve accuracy

Answer

B) To give services time to recover between retries

Explanation: Exponential backoff (1s, 2s, 4s, 8s…) prevents overwhelming struggling services with rapid retry attempts.

Question 9

Which LangChain component manages the agent’s decision-making?

  • A) Tools
  • B) Memory
  • C) AgentExecutor
  • D) Chains

Answer

C) AgentExecutor

Explanation: The AgentExecutor runs the agent loop, deciding which tools to call and when to stop.

Question 10

What is “self-correction” in the ReAct pattern?

  • A) Fixing syntax errors
  • B) The agent detecting mistakes and trying different approaches
  • C) Error handling in code
  • D) Validating inputs

Answer

B) The agent detecting mistakes and trying different approaches

Explanation: Self-correction occurs when the agent sees an error/unexpected result and reasons about how to fix it, then tries again.

Advanced Questions (Bonus)

Question 11

What’s the primary difference between LangChain and LangGraph?

  • A) LangChain is faster
  • B) LangGraph provides graph-based workflow control
  • C) LangChain is newer
  • D) There’s no difference

Answer

B) LangGraph provides graph-based workflow control

Explanation: LangGraph extends LangChain with graph-based state management, enabling more complex agent architectures with cycles and conditionals.

Question 12

In agent memory, what is a “vector database” used for?

  • A) Storing images
  • B) Semantic search over past information
  • C) Faster SQL queries
  • D) Caching API calls

Answer

B) Semantic search over past information

Explanation: Vector databases store embeddings, allowing semantic similarity search to retrieve relevant past memories.

Question 13

What problem does “token usage tracking” solve?

  • A) Prevents API rate limits
  • B) Monitors costs and optimizes context length
  • C) Improves accuracy
  • D) Speeds up responses

Answer

B) Monitors costs and optimizes context length

Explanation: Tracking tokens helps control costs (tokens = money) and prevents context overflow errors.

Question 14

In multi-agent systems, what is “delegation”?

  • A) Deleting agents
  • B) One agent assigning tasks to other agents
  • C) Error handling
  • D) Parallel execution

Answer

B) One agent assigning tasks to other agents

Explanation: Delegation allows a coordinator agent to distribute work to specialized agents based on their capabilities.

Question 15

What’s a key limitation of current AI agents?

  • A) They’re too expensive
  • B) They can hallucinate and make mistakes
  • C) They’re too slow
  • D) They require too much code

Answer

B) They can hallucinate and make mistakes

Explanation: LLMs can generate incorrect information confidently. Agents need validation, error handling, and human oversight for critical tasks.

Self-Check Guide

Basic Questions (1-10)

  • 0-5 correct: Review the notebooks; some concepts still need reinforcement.
  • 6-7 correct: Good understanding; practice with challenges next.
  • 8-9 correct: Strong grasp of concepts and ready for more realistic builds.
  • 10 correct: Excellent command of the fundamentals.

Advanced Questions (11-15)

  • 0-2 correct: Focus on the advanced notebooks and frameworks.
  • 3-4 correct: Solid advanced knowledge; keep practicing.
  • 5 correct: Strong advanced understanding and ready for complex agent builds.

Compare Your Growth

Pre-Quiz Score: _____ / 10

Post-Quiz Score: _____ / 15

Improvement: Look at questions you got wrong initially - did you get them right this time?

Next Steps

Based on your score:

If you scored 8+/10 on basics:

  • ✅ Complete the assignment (build your own agent)
  • ✅ Try advanced challenges (multi-agent, memory)
  • ✅ Explore LangChain/LangGraph in depth
  • ✅ Build a production agent for a real use case

If you scored 5-7/10:

  • 📚 Review notebooks 2-4 (function calling, ReAct, frameworks)
  • 🛠️ Complete challenges 1-4
  • 💪 Practice with more examples
  • 🔄 Retake quiz in a few days

If you scored below 5/10:

  • 🔄 Re-read notebooks from the beginning
  • ✏️ Type out all code examples (don’t just read)
  • 🤔 Post focused questions in GitHub Discussions or write down the exact point of confusion
  • 📝 Take notes on key concepts
  • 🎯 Focus on notebooks 1-3 first

Key Concepts to Master

Make sure you understand:

  • Difference between chatbots and agents
  • How function calling works
  • Tool schema design (name, description, parameters)
  • Input validation strategies
  • ReAct pattern (Thought → Action → Observation)
  • Error handling and self-correction
  • Agent memory (short-term vs long-term)
  • Multi-agent coordination
  • Using LangChain for agents
  • Production considerations (logging, rate limiting, caching)

Feedback

Which topics were:

  • Clearest? _______________
  • Most confusing? _______________
  • Most useful? _______________
  • Want to learn more about? _______________

Congratulations on completing the AI Agents phase! 🎉

You now have the skills to build intelligent, autonomous agents that can solve complex, multi-step problems. Time to build something amazing! 🚀

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