Branching and conditional logic in Agentic AI - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of conditional branching in agentic AI decision

What is the output of the following agentic AI decision logic code snippet?

Agentic AI

def agent_decision(score):
    if score > 80:
        return 'High priority'
    elif score > 50:
        return 'Medium priority'
    else:
        return 'Low priority'

result = agent_decision(65)
print(result)

A "Medium priority"

B "No priority"

C "Low priority"

D "High priority"

💡 Hint

Check which condition the score 65 satisfies in the if-elif-else chain.

The score 65 is greater than 50 but not greater than 80, so the elif branch runs returning 'Medium priority'.

❓ Model Choice

intermediate

Choosing the correct branching model for agentic AI

Which model architecture best supports branching decisions based on multiple conditions in agentic AI?

A A decision tree model

B A convolutional neural network

C A simple linear regression model

D A k-means clustering model

💡 Hint

Consider which model naturally splits data based on conditions.

Decision trees split data by branching on conditions, making them ideal for branching logic.

❓ Hyperparameter

advanced

Hyperparameter affecting branching depth in decision trees

Which hyperparameter controls the maximum depth of branching in a decision tree model?

A learning_rate

B batch_size

C max_depth

D n_estimators

💡 Hint

This hyperparameter limits how many splits the tree can have from root to leaf.

max_depth limits the number of branching levels in a decision tree.

❓ Metrics

advanced

Metric to evaluate branching decision accuracy

Which metric best measures the accuracy of branching decisions in a classification model?

A Perplexity

B Accuracy score

C Silhouette score

D Mean squared error

💡 Hint

This metric counts correct predictions over total predictions.

Accuracy score measures the proportion of correct classifications, suitable for branching decisions.

🔧 Debug

expert

Debugging incorrect branching in agentic AI code

What error does this agentic AI branching code produce? ```python def agent_response(value): match value: case x if x > 10: return 'High' case x if x > 5: return 'Medium' case _: return 'Low' print(agent_response(7)) ```

Agentic AI

def agent_response(value):
    match value:
        case x if x > 10:
            return 'High'
        case x if x > 5:
            return 'Medium'
        case _:
            return 'Low'

print(agent_response(7))

A SyntaxError due to invalid match-case syntax

B 'High' output

C 'Low' output

D 'Medium' output

💡 Hint

Check which case matches value 7 first in the match-case with guards.

7 is not > 10, so first case fails; 7 > 5 is true, so second case returns 'Medium'.