10 Google Dialogflow Interview Questions and Answers

Google Dialogflow is a leading platform for building conversational interfaces, including chatbots and voice applications. Leveraging natural language processing (NLP) and machine learning, Dialogflow enables developers to create intuitive and responsive user interactions. Its integration capabilities with various platforms and services make it a versatile tool for enhancing customer engagement and automating support tasks.

This article offers a curated selection of interview questions designed to test your knowledge and proficiency with Google Dialogflow. By reviewing these questions and their detailed answers, you will be better prepared to demonstrate your expertise and problem-solving abilities in any technical interview setting.

Google Dialogflow Interview Questions and Answers

1. Explain the role of Entities in Dialogflow and provide an example of a custom entity.

Entities in Dialogflow help in extracting and categorizing information from user inputs. For example, in the query “Book a flight from New York to San Francisco,” entities identify “New York” as the departure city and “San Francisco” as the destination. Custom entities are useful for handling domain-specific data not covered by system entities, such as menu items in a restaurant chatbot.

Example of a custom entity:

{
  "name": "OrderFood",
  "trainingPhrases": [
    {
      "parts": [
        {
          "text": "I would like to order a ",
          "entityType": "@MenuItem",
          "alias": "foodItem"
        }
      ]
    }
  ],
  "parameters": [
    {
      "name": "foodItem",
      "entityType": "@MenuItem",
      "value": "$foodItem"
    }
  ]
}

In this example, “Pizza” is recognized as a value of the “MenuItem” entity and captured as the parameter “foodItem.”

2. How would you handle context in a conversation flow? Provide a brief code snippet.

Contexts in Dialogflow manage the state of a conversation, influencing its flow by setting and retrieving context parameters. They act as flags indicating the current state or topic and can expire after a set number of interactions.

Example of handling context in a conversation flow:

from flask import Flask, request, jsonify

app = Flask(__name__)

@app.route('/webhook', methods=['POST'])
def webhook():
    req = request.get_json(silent=True, force=True)
    intent = req.get('queryResult').get('intent').get('displayName')
    
    if intent == 'BookFlight':
        return jsonify({
            'fulfillmentText': 'Where would you like to fly?',
            'outputContexts': [{
                'name': f"{req.get('session')}/contexts/awaiting_destination",
                'lifespanCount': 5
            }]
        })
    elif intent == 'ProvideDestination':
        context = req.get('queryResult').get('outputContexts')
        for ctx in context:
            if 'awaiting_destination' in ctx.get('name'):
                destination = req.get('queryResult').get('parameters').get('geo-city')
                return jsonify({
                    'fulfillmentText': f'You want to fly to {destination}. When would you like to travel?'
                })
    
    return jsonify({'fulfillmentText': 'I did not understand that.'})

if __name__ == '__main__':
    app.run(debug=True)

In this example, the context awaiting_destination is set when the BookFlight intent is triggered and checked in the ProvideDestination intent.

3. Describe how Fulfillment works and write a simple webhook in Node.js that returns a static response.

Fulfillment in Dialogflow connects your agent to external services for dynamic responses. When an intent with fulfillment is matched, Dialogflow sends a request to your webhook, which processes it and sends back a response.

Example of a simple Node.js webhook:

const express = require('express');
const bodyParser = require('body-parser');

const app = express();
app.use(bodyParser.json());

app.post('/webhook', (req, res) => {
    const response = {
        fulfillmentText: 'This is a static response from the webhook'
    };
    res.json(response);
});

const PORT = process.env.PORT || 3000;
app.listen(PORT, () => {
    console.log(`Server is running on port ${PORT}`);
});

This webhook listens for POST requests at the /webhook endpoint and returns a static response.

4. Write a function in Python that handles a Dialogflow webhook request and extracts the intent name.

To extract the intent name from a Dialogflow webhook request, parse the JSON payload sent by Dialogflow. Use Python’s built-in libraries to handle the JSON data.

Example:

import json
from flask import Flask, request

app = Flask(__name__)

@app.route('/webhook', methods=['POST'])
def webhook():
    req = request.get_json(silent=True, force=True)
    intent_name = req.get('queryResult').get('intent').get('displayName')
    return f"Intent: {intent_name}"

if __name__ == '__main__':
    app.run(debug=True)

5. Explain the purpose of training phrases and how they impact intent detection.

Training phrases in Dialogflow are sample sentences that help the agent understand user intentions. They improve intent detection by providing examples of how users might phrase their requests, allowing the NLU model to generalize and recognize the intent even if the input doesn’t exactly match any training phrases.

For instance, for a flight booking intent, training phrases might include:

• Book a flight to New York
• I need a flight to Los Angeles
• Can you find me a flight to Chicago?

These examples help the model understand the intent to book a flight, regardless of specific wording.

6. How would you implement slot filling in Dialogflow? Provide a brief explanation and a code example.

Slot filling in Dialogflow collects multiple pieces of information from the user to fulfill an intent. When an intent is triggered, Dialogflow prompts the user for required parameters until all necessary information is gathered.

Example:

• Create an intent, e.g., “BookFlight”.
• Define required parameters like “destination”, “departure_date”, and “return_date”.
• Set prompts for each parameter.

{
  "name": "projects/<Project-ID>/agent/intents/<Intent-ID>",
  "displayName": "BookFlight",
  "parameters": [
    {
      "displayName": "destination",
      "entityTypeDisplayName": "@sys.geo-city",
      "mandatory": true,
      "prompts": [
        "Where would you like to fly to?"
      ]
    },
    {
      "displayName": "departure_date",
      "entityTypeDisplayName": "@sys.date",
      "mandatory": true,
      "prompts": [
        "When do you want to depart?"
      ]
    },
    {
      "displayName": "return_date",
      "entityTypeDisplayName": "@sys.date",
      "mandatory": true,
      "prompts": [
        "When do you want to return?"
      ]
    }
  ]
}

7. Write a Python script that uses the Dialogflow API to detect intent from a given text input.

To detect intent from a text input using the Dialogflow API, set up the client, send a text query, and process the response. Below is a Python script demonstrating these steps:

import dialogflow_v2 as dialogflow

def detect_intent_texts(project_id, session_id, texts, language_code):
    session_client = dialogflow.SessionsClient()
    session = session_client.session_path(project_id, session_id)

    for text in texts:
        text_input = dialogflow.types.TextInput(text=text, language_code=language_code)
        query_input = dialogflow.types.QueryInput(text=text_input)
        response = session_client.detect_intent(session=session, query_input=query_input)
        
        print('Query text:', response.query_result.query_text)
        print('Detected intent:', response.query_result.intent.display_name)
        print('Detected intent confidence:', response.query_result.intent_detection_confidence)
        print('Fulfillment text:', response.query_result.fulfillment_text)

# Example usage
project_id = 'your-project-id'
session_id = 'your-session-id'
texts = ['Hello', 'I need help with my order']
language_code = 'en'

detect_intent_texts(project_id, session_id, texts, language_code)

8. Explain how to use follow-up intents and provide a scenario where they would be useful.

Follow-up intents in Dialogflow manage multi-turn conversations by creating a parent-child relationship between intents. When a user triggers a parent intent, follow-up intents are activated based on subsequent responses, allowing for more natural interactions.

For example, in a customer service chatbot, a user might start by asking about their order status. The initial intent could be “Check Order Status.” Based on the user’s response, such as providing an order number, a follow-up intent like “Order Number Provided” can be triggered to fetch and display the order status.

9. Describe how to use rich responses in Dialogflow and provide an example.

Rich responses in Dialogflow provide more engaging replies to users, including elements like images, cards, quick replies, and custom payloads. They enhance the user experience by making interactions more visually appealing.

Example:

{
  "fulfillmentMessages": [
    {
      "card": {
        "title": "Welcome to our service",
        "subtitle": "How can we assist you today?",
        "imageUri": "https://example.com/image.png",
        "buttons": [
          {
            "text": "Get Started",
            "postback": "https://example.com/get-started"
          }
        ]
      }
    }
  ]
}

In this example, a card response welcomes the user, provides a subtitle, displays an image, and includes a button directing the user to a URL.

10. How do you handle errors and fallbacks in Dialogflow to ensure a smooth user experience?

Handling errors and fallbacks in Dialogflow is important for maintaining a smooth user experience. Dialogflow provides several mechanisms to manage these scenarios:

• Default Fallback Intents: Built-in fallback intents are triggered when the user’s input doesn’t match any defined intents. These can be customized to provide helpful messages.
• Custom Fallback Intents: Create custom fallback intents tailored to specific contexts or inputs for more granular control over the conversation flow.
• Context Management: Contexts help manage the state of the conversation, allowing for graceful error handling. For example, if a user provides unexpected input, contexts can prompt for clarification.
• Event Handling: Define events triggered by specific conditions or inputs to handle errors and fallbacks dynamically.
• Training Phrases and Entity Recognition: Continuously updating training phrases and entity recognition can reduce errors by improving the model’s understanding of user inputs.