Sequential step execution in Agentic AI - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of this sequential step execution code?

Consider the following Python code simulating sequential steps in an AI agent. What will be printed after running it?

Agentic AI

steps = ['load data', 'preprocess', 'train model', 'evaluate']
results = []
for step in steps:
    results.append(f"Completed: {step}")
print(results[-1])

A Completed: train model

B Completed: evaluate

C Completed: preprocess

D Completed: load data

💡 Hint

Look at the last item appended to the results list.

The loop appends a message for each step. The last step is 'evaluate', so the last printed message is 'Completed: evaluate'.

❓ Model Choice

intermediate

Which model architecture best fits a sequential step execution task?

You want to design an AI agent that processes tasks step-by-step in order. Which model type is best suited for this?

A Convolutional Neural Network (CNN)

B Autoencoder

C Feedforward Neural Network

D Recurrent Neural Network (RNN)

💡 Hint

Think about models that handle sequences and remember past steps.

RNNs are designed to process sequences step-by-step, keeping track of previous information, making them ideal for sequential tasks.

❓ Hyperparameter

advanced

Which hyperparameter controls how much past information is kept in a sequential model?

In a recurrent neural network, which hyperparameter affects how much the model remembers from previous steps?

A Hidden state size

B Batch size

C Learning rate

D Dropout rate

💡 Hint

This parameter determines the memory capacity of the model's internal state.

The hidden state size controls how much information the model can store and use from previous steps in the sequence.

❓ Metrics

advanced

Which metric best evaluates step-by-step prediction accuracy in sequential tasks?

You have a model predicting the next step in a sequence. Which metric best measures how often the model predicts the correct next step?

A Accuracy

B F1 Score

C Mean Squared Error (MSE)

D Perplexity

💡 Hint

Think about a metric that counts correct predictions out of total predictions.

Accuracy measures the proportion of correct predictions, which is suitable for step-by-step classification tasks.

🔧 Debug

expert

Why does this sequential step execution code raise an error?

Examine the code below. Why does it raise an error when run?

Agentic AI

steps = ['step1', 'step2', 'step3']
results = []
for i in range(len(steps)+1):
    results.append(steps[i])
print(results)

A ValueError because range argument is invalid

B TypeError because steps is not iterable

C IndexError because the loop tries to access an index outside the list range

D SyntaxError due to missing colon in for loop

💡 Hint

Check the range used in the loop compared to the list length.

The loop runs one step too many, causing an IndexError when accessing steps[i] with i equal to the list length.