Customer support agent architecture in Agentic Ai - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 conceptual

intermediate

Understanding the role of intent recognition in customer support agents

In a customer support agent architecture, what is the primary purpose of the intent recognition component?

A To generate a detailed response based on customer sentiment

B To identify the main goal or request behind a customer's message

C To store customer data securely for future use

D To translate customer messages into multiple languages

❓ model choice

intermediate

Choosing the best model for sentiment analysis in customer support

Which type of model is most suitable for analyzing customer sentiment in a support chat to detect frustration or satisfaction?

A Recurrent Neural Network (RNN) with LSTM layers trained on customer reviews

B Convolutional Neural Network (CNN) trained on text data

C K-Means clustering model for grouping customer messages

D Linear regression model predicting response time

💻 code output

advanced

Output of a simple intent classification code snippet

What is the output of the following Python code that classifies customer intents using a simple keyword check?

Agentic_ai

def classify_intent(message):
    if 'refund' in message.lower():
        return 'Request Refund'
    elif 'price' in message.lower():
        return 'Ask Price'
    else:
        return 'General Inquiry'

print(classify_intent('Can I get a refund for my order?'))

A None

B Ask Price

C General Inquiry

D Request Refund

❓ metrics

advanced

Evaluating model performance with precision and recall

A customer support intent classifier has the following results on a test set: 80 true positives, 20 false positives, and 40 false negatives. What is the precision and recall of the model?

A Precision: 0.80, Recall: 0.67

B Precision: 0.67, Recall: 0.80

C Precision: 0.75, Recall: 0.75

D Precision: 0.50, Recall: 0.80

🔧 debug

expert

Identifying the cause of incorrect response generation in a customer support agent

A customer support agent uses a sequence-to-sequence model to generate responses. The model often repeats the same phrase multiple times in its output. What is the most likely cause of this behavior?

A The model uses dropout layers during inference, causing instability

B The input messages are too short, leading to incomplete context

C The model's beam search decoding is not properly configured, causing repetitive loops

D The training data contains too many unique responses, confusing the model