Question 1

What should the engineer focus on when adding a new feature?

❌ Minimize code lines
❌ Make it operate independently
❌ Maximize processing speed
✅ Ensure the new feature can communicate effectively with existing agents

Explanation:
In agentic systems, interoperability is critical—new components must integrate smoothly with existing agents.

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Question 2

How to analyze the benefits of agentic AI in logistics?

❌ Evaluate single static task performance
✅ Assess the system’s ability to adapt independently to dynamic environments
❌ Compare computational resources
❌ Aim to replace human decision makers

Explanation:
Agentic AI is powerful because it handles dynamic, changing environments with autonomy.

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Question 3

How should Sarah ensure effective communication between agents?

✅ Implement a shared knowledge base for agents to access common data
❌ Use a centralized server
❌ Assign unique, isolated tasks
❌ Use hierarchical command control

Explanation:
A shared memory or knowledge base ensures consistency and smooth agent ↔ agent interaction.

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Question 4

How should David structure the agents in LangGraph?

❌ Fixed agents
❌ One multitasking agent
✅ Design modular agents that can be dynamically activated
❌ Round-robin task distribution

Explanation:
Modular, reconfigurable agents allow flexibility and scalability for dynamic tasks.

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Question 5

How can Alex apply parallelization in LangGraph?

✅ Distribute data processing tasks across multiple nodes
❌ Single optimized pipeline
❌ Single fast node
❌ Sequential but optimized

Explanation:
Parallelization = splitting work across nodes for simultaneous processing.

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Question 6

Advantage of the orchestrator design pattern?

❌ Removes coordinator
❌ Makes agents independent
❌ Removes communication
✅ Provides centralized control for coordinating multiple agents

Explanation:
The orchestrator directs agent interactions and ensures consistent workflow management.

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Question 7

How should Tom use the state variable in Evaluator–Optimizer pattern?

✅ Store feedback, iteration count, and risk grades for tracking improvement
❌ Store only final outputs
❌ Store only plan & profile strings
❌ Store user inputs/logs

Explanation:
The state must capture learning history to refine the model across iterations.

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🧾 Summary Table