Why agents represent the next AI paradigm in Agentic Ai - Challenge Your Understanding

Challenge - 5 Problems

🧠 conceptual

intermediate

Understanding Agent Autonomy

Which statement best describes why agents are considered autonomous in AI?

A Agents operate solely based on random actions without any goal.

B Agents require constant human input to perform tasks effectively.

C Agents only follow pre-programmed instructions without adapting to new data.

D Agents can independently perceive their environment and make decisions without human intervention.

❓ model choice

intermediate

Choosing Agent Architecture

You want to build an AI agent that can learn from experience and improve over time. Which model architecture is best suited?

A Reinforcement learning model that learns from rewards and penalties.

B Rule-based system with fixed if-then rules.

C Static decision tree that does not update after training.

D Simple linear regression model predicting a fixed output.

❓ metrics

advanced

Evaluating Agent Performance

Which metric is most appropriate to evaluate an AI agent's success in completing a sequence of tasks over time?

A Cumulative reward collected during task execution.

B Accuracy measured on a single static dataset.

C Mean squared error between predicted and actual values.

D Precision and recall on a classification problem.

🔧 debug

advanced

Debugging Agent Action Selection

Given this pseudocode for an agent's action selection, which bug causes the agent to always pick the first action?

actions = ['move', 'stop', 'turn']
selected_action = None
for action in actions:
    if action == 'move':
        selected_action = action
        break
    else:
        selected_action = actions[0]

A The else clause resets selected_action to the first action every time.

B The actions list is empty, so no action is selected.

C The break statement causes the loop to exit after the first action is checked.

D The loop never assigns any action to selected_action.

💻 code output

expert

Agent Decision Output

What is the output of this Python code simulating a simple agent decision process?

class Agent:
    def __init__(self):
        self.state = 0
    def act(self, observation):
        match observation:
            case 'obstacle':
                self.state += 1
                return 'turn'
            case 'clear':
                self.state = 0
                return 'move'
            case _:
                return 'stop'

agent = Agent()
outputs = []
inputs = ['clear', 'obstacle', 'obstacle', 'clear', 'unknown']
for inp in inputs:
    outputs.append(agent.act(inp))
print(outputs)

A ['move', 'turn', 'turn', 'turn', 'stop']

B ['move', 'turn', 'turn', 'move', 'stop']

C ['stop', 'turn', 'turn', 'move', 'stop']

D ['move', 'move', 'move', 'move', 'stop']