Why production agents need different architecture in Agentic Ai - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to define a production agent's main loop.

Agentic_ai

while agent.is_active():
    action = agent.[1](input_data)
    agent.execute(action)

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Adecide_action

Btrain_model

Cload_data

Dsave_state

Attempts:

3 left

2fill in blank

medium

Complete the code to initialize a production agent with a specific architecture.

Agentic_ai

agent = ProductionAgent(architecture=[1])

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A'simple_nn'

B'reactive_architecture'

C'random_forest'

D'linear_regression'

Attempts:

3 left

3fill in blank

hard

Fix the error in the code that updates the agent's state in production.

Agentic_ai

def update_state(self, input):
    self.state = self.state [1] input
    return self.state

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A*=

B=

C-=

D+=

Attempts:

3 left

4fill in blank

hard

Fill in the blank to create a dictionary comprehension that filters agent logs for errors.

Agentic_ai

error_logs = {log_id: log for log_id, log in logs.items() if [1] in log}

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A'error'

Bcontains

Cstartswith

D'warning'

Attempts:

3 left

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps agent IDs to their status if active.

Agentic_ai

active_agents = {agent[1]: agent[2] for agent in agents if agent.status [3] 'active'}

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A.id

B.status

C==

D!=

Attempts:

3 left