20 Rasa Chatbot Interview Questions and Answers

If you’re applying for a position that involves Rasa Chatbot, you should expect to answer some questions about the popular open-source chatbot framework during your interview. Rasa Chatbot is used by developers to build and deploy chatbots on a variety of platforms, such as Facebook Messenger, Slack, and Telegram. In this article, we’ll review some common Rasa Chatbot interview questions and how you can answer them.

Rasa Chatbot Interview Questions and Answers

Here are 20 commonly asked Rasa Chatbot interview questions and answers to prepare you for your interview:

1. What is Rasa?

Rasa is an open source chatbot framework that helps developers build conversational AI applications.

2. How does Rasa work?

Rasa is an open source chatbot framework that is designed to be easily customizable. It is based on natural language processing and machine learning, and it allows developers to create chatbots that can understand and respond to human conversation. Rasa chatbots can be deployed on a variety of platforms, including Facebook Messenger, Slack, and Telegram.

3. Can you give me some examples of how Rasa can be used in real-world scenarios?

Rasa can be used for a wide range of tasks, including customer service, sales, marketing, and even IT support. For customer service, Rasa can be used to create a chatbot that can handle customer queries and complaints. For sales, Rasa can be used to create a chatbot that can provide information about products and services, as well as handle sales inquiries. For marketing, Rasa can be used to create a chatbot that can interact with customers and provide information about promotions and discounts. And for IT support, Rasa can be used to create a chatbot that can help users troubleshoot technical issues.

4. What are the high level steps involved in building a chatbot with Rasa?

The high level steps involved in building a chatbot with Rasa are:

1. Define the domain of your chatbot
2. Train a Rasa model
3. Connect your chatbot to a channel (e.g. Facebook Messenger)
4. Test your chatbot!

5. What is your understanding of intent classification and entity extraction in the context of natural language processing?

Intent classification is the process of determining what the user wants to achieve with their input, while entity extraction is the process of identifying and extracting relevant information from the user input. In the context of chatbots, these two tasks are essential in order to understand the user’s intent and fulfill their request.

6. What is an intent? Give me an example of one.

An intent is a goal or purpose that a user has when interacting with a chatbot. For example, a user’s intent might be to get information about a product, make a purchase, or schedule a meeting.

7. How do intents differ from entities?

Intents are the overall goals or desires that a user has, while entities are the specific details or pieces of information that a user provides in order to complete an intent. For example, if a user wants to book a hotel room, the intent would be “booking” and the entities would be things like “location,” “number of nights,” and “room type.”

8. What is the process for updating or deleting stories after they’ve been created in Rasa?

After you have created a story in Rasa, you can update it by going into the story file and making the necessary changes. To delete a story, you will need to remove it from the story file entirely.

9. Can you explain what a story generator is?

A story generator is a piece of software that helps you create stories. It can be used to create plots, characters, dialogue, and more.

10. What’s the difference between NLU, Core, and Slack/Facebook connectors?

NLU is responsible for natural language understanding, which involves taking user input and extracting meaning from it. Core is the part of the chatbot that contains the dialogue management logic, which determines how the chatbot responds to user input. The Slack/Facebook connectors allow the chatbot to be used on those platforms.

11. Can you explain what Natural Language Understanding (NLU) is?

NLU is a process of extracting meaning from text, in order to better understand the user’s intent. This is done through a variety of methods, including but not limited to: part-of-speech tagging, dependency parsing, and named entity recognition.

12. How can you use rasa nlu to build custom components?

You can use rasa nlu to build custom components by training your own models to recognize custom entities, intents, and utterances. You can also use rasa nlu to build custom webhooks to integrate your chatbot with third-party services.

13. What is the significance of model configuration files in Rasa?

The model configuration files in Rasa are responsible for specifying the details of the chatbot model that is being created. This includes specifying the language that the chatbot will be using, the training data that will be used to train the model, and the parameters that will be used to tune the model.

14. What types of actions can be taken by an assistant built with Rasa?

Rasa provides a wide range of capabilities for building chatbots, including the ability to handle small talk, provide information, make recommendations, and schedule appointments.

15. What are the various components of a domain in Rasa?

The various components of a domain in Rasa include intents, entities, slots, actions, templates, and config. Intents are the user’s goals or desired outcomes, entities are the key pieces of information that are required to fulfill a user’s intent, slots are used to track information throughout a conversation, actions are the steps taken to fulfill a user’s intent, templates are used to generate responses, and config is used to configure the domain.

16. What are the different types of slots supported by Rasa?

Rasa supports four types of slots: text, float, bool, and list. Text slots can store any kind of text, float slots can store numbers, bool slots can store true/false values, and list slots can store lists of values.

17. Is it possible to specify multiple utterances that map to the same intent in Rasa? If yes, then how?

Yes, it is possible to specify multiple utterances that map to the same intent in Rasa. You can do this by providing multiple examples of utterances for each intent in your training data. Rasa will then learn to map those utterances to the correct intent.

18. What are the best practices for developing Rasa chatbots?

There are a few best practices to keep in mind when developing Rasa chatbots. First, it is important to keep the chatbot’s conversation flow natural and easy to follow. Second, make sure to design the chatbot’s responses in a way that is helpful and informative, without being too pushy or sales-y. Finally, it is important to test the chatbot thoroughly before deploying it, in order to ensure that it is functioning properly.

19. What is the best way to detect duplicate intents?

The best way to detect duplicate intents is to use a tool like Rasa NLU Inspector. This tool will allow you to see all of the intents that are being predicted by your chatbot, and will highlight any that are duplicates. This is a great way to clean up your chatbot’s intents and make sure that each one is unique.

20. What is the role of Rasa X in the context of creating chatbots?

Rasa X is a tool that helps you build, improve, and deploy chatbots powered by the Rasa framework. It provides a graphical user interface that makes it easy to train and test your chatbot, as well as to deploy it to a production environment.