Saffron Tech Interview Questions and Answers

React hooks are functions that let you use state and lifecycle features in functional components.

• useState: Allows you to add state to functional components. Example: const [count, setCount] = useState(0);

• useEffect: Lets you perform side effects in function components. Example: useEffect(() => { document.title = `Count: ${count}`; }, [count]);

• useContext: Provides a way to pass data through the component tree without ...read more

In five years, I envision myself as a skilled professional, contributing significantly to my field and leading innovative projects.

• I aim to have advanced my skills through continuous learning and professional development, such as obtaining relevant certifications.

• I see myself taking on leadership roles, perhaps managing a team or leading projects that drive impactful results.

• I hope to be involved in innovative projects...read more

I tend to be overly critical of my work, which can slow down my progress but helps me maintain high standards.

• I often spend extra time reviewing my work to ensure it's perfect, which can lead to missed deadlines.

• For example, during a group project, I focused too much on perfecting my section, causing delays for the team.

• I've learned to set time limits for revisions to balance quality and efficiency.

• I also seek feedback...read more

Supervised learning is a machine learning approach where a model is trained on labeled data to make predictions or classifications.

• Involves training a model on a dataset with input-output pairs.

• Common algorithms include linear regression, decision trees, and support vector machines.

• Used for tasks like spam detection (classifying emails) and image recognition (identifying objects in images).

• The model learns from the lab...read more

MSE, or Mean Squared Error, is a common metric used to measure the average squared difference between predicted and actual values.

• MSE is calculated as the average of the squares of the errors: MSE = (1/n) * Σ(actual - predicted)².

• A lower MSE indicates a better fit of the model to the data.

• For example, if actual values are [3, -0.5, 2] and predicted values are [2.5, 0.0, 2], MSE = (1/3) * ((3-2.5)² + (-0.5-0)² + (2-2)²)...read more