20 Flowchart Interview Questions and Answers

Flowcharts are a type of diagram that visually represent an algorithm, process or workflow. They are often used in business and software engineering to document, design and communicate complex systems. If you are interviewing for a position that involves flowcharting, it is important to be prepared to answer questions about your experience and knowledge. In this article, we review some common flowchart interview questions and provide tips on how to answer them.

Flowchart Interview Questions and Answers

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

1. What is a flowchart?

A flowchart is a graphical representation of a process or workflow. It is typically used to map out a process so that it can be easily followed and understood. Flowcharts can be used for a variety of purposes, from mapping out a company’s organizational structure to illustrating a computer program’s steps.

2. How do you create a flowchart for an algorithm?

The first step is to identify the inputs and outputs of the algorithm. Then, you need to determine what the steps of the algorithm are. Once you have that information, you can start creating the flowchart. Each step of the algorithm will be represented by a different shape, and the inputs and outputs will be represented by arrows.

3. What are some common symbols used in flowcharts?

Some common symbols used in flowcharts include rectangles (for processes), diamonds (for decision points), and arrows (for showing the flow of the process).

4. Can you explain what a decision symbol is in the context of flowcharts?

A decision symbol is a symbol that is used in a flowchart to represent a decision that needs to be made. This symbol is usually represented by a diamond shape.

5. When would you use a start/end symbol in your flowchart?

A start/end symbol would be used to indicate the beginning and end of a process. This is useful for showing the overall structure of a process, and can be helpful for understanding how different parts of a process fit together.

6. Why should we avoid using more than one connector at a time?

There are a few reasons for why it is generally advised to avoid using more than one connector at a time in a flowchart. First, it can make the flowchart more difficult to read and understand. Second, it can make it more difficult to make changes to the flowchart later on, as all of the different connectors can become tangled together. Finally, it can also make it more difficult to export the flowchart to a different format, as some formats may not support multiple connectors.

7. What’s the difference between a sequence and selection structure?

A sequence structure is a set of instructions that are carried out in order, one after the other. A selection structure, on the other hand, allows you to choose which set of instructions to carry out based on a certain condition.

8. What are two advantages of creating a flowchart to represent our process or program?

Flowcharts can be useful for a number of reasons. First, they can help us to better understand a process or program by breaking it down into smaller, more manageable steps. Additionally, flowcharts can also be helpful in identifying potential bottlenecks or areas of improvement within a process.

9. What are some ways to make sure that we don’t have any unnecessary repetition in our flowchart?

One way to avoid repetition in a flowchart is to use a subroutine. This allows you to define a section of the flowchart that can be called upon as needed, rather than duplicating the entire section each time. Another way to avoid repetition is to use loops, which allows you to repeat a section of the flowchart a certain number of times or until a certain condition is met.

10. What is the most popular programming language used by data scientists these days?

There is no definitive answer to this question, as data scientists often use a variety of programming languages depending on the specific tasks they are working on. However, some of the most popular languages used by data scientists include Python, R, and MATLAB.

11. What is back propagation?

Back propagation is a method of training artificial neural networks. It is a type of supervised learning, where the desired output is known in advance, and the network adjusts its weights and biases accordingly in order to produce the desired output.

12. What are three things you need to know about a neural network before building one?

1. The number of input and output nodes.
2. The number of hidden layers and the number of nodes in each hidden layer.
3. The activation function for each node.

13. What do you understand about convolutional neural networks?

Convolutional neural networks are a type of neural network that are particularly well-suited for image recognition tasks. This is because they are able to learn features from the data that are relevant for the task at hand, and they are also able to reduce the amount of data that needs to be processed by only considering the relevant parts of the image.

14. What are generative adversarial networks?

Generative adversarial networks are a type of neural network used for unsupervised learning. They are made up of two parts: a generator network and a discriminator network. The generator network creates new data, while the discriminator network evaluates the data and tries to determine whether it is real or fake. The two networks compete with each other, and over time the generator network gets better at creating data that is indistinguishable from real data.

15. What is the difference between artificial intelligence, machine learning, and deep learning?

Artificial intelligence is a field of computer science that deals with creating intelligent agents, which are systems that can reason, learn, and act autonomously. Machine learning is a subset of artificial intelligence that deals with the creation of algorithms that can learn from and make predictions on data. Deep learning is a subset of machine learning that deals with the creation of algorithms that can learn from data that is unstructured or unlabeled.

16. What are some examples of when it makes sense to use reinforcement learning?

There are many different types of reinforcement learning, but one example is when you are trying to teach a machine how to play a game. In this case, the machine is constantly trying different actions and being rewarded or punished based on its success. Over time, it will learn which actions are more likely to lead to success, and this can be applied to other games or tasks.

17. In what scenarios is it better to use deep learning instead of machine learning?

There is no definitive answer to this question, as it depends on the specific data and problem at hand. However, in general, deep learning is better suited for problems that are highly nonlinear in nature, or where there is a lot of data that needs to be processed. Additionally, deep learning is often more effective than machine learning when it comes to tasks like image recognition and natural language processing.

18. How important is feature extraction in the context of deep learning?

Feature extraction is a critical part of deep learning, as it is responsible for identifying the relevant features in data that can be used to train a model. Without effective feature extraction, it would be very difficult to train a deep learning model that could generalize well to new data.

19. What is cross validation in the context of machine learning?

Cross validation is a technique used to assess the accuracy of a machine learning model. It works by splitting the data into a training set and a test set, then training the model on the training set and testing it on the test set. This process is repeated a number of times, and the average accuracy is used to assess the model.

20. What are some other types of neural networks beyond feed forward fully connected ones?

There are a number of different types of neural networks, each with their own advantages and disadvantages. Some of the more popular types include recurrent neural networks, convolutional neural networks, and long short-term memory networks.