Veltris Interview Questions and Answers

Q1. Difference between Let, Var, Const
Ans. 
Let, Var, and Const are JavaScript keywords used for declaring variables with different scoping rules and mutability.
• Var has function scope and can be redeclared and reassigned.

• Let has block scope and can be reassigned but not redeclared.

• Const has block scope and cannot be reassigned or redeclared.

• Use const for values that won't change, let for values that will, and var for legacy code or global variables.
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Q2. Life Cycle of React-Native
Ans. 
React Native has several life cycle methods that are called at different stages of the component's life.
• Mounting: constructor(), componentWillMount(), render(), componentDidMount()

• Updating: componentWillReceiveProps(), shouldComponentUpdate(), componentWillUpdate(), render(), componentDidUpdate()

• Unmounting: componentWillUnmount()

• Error Handling: componentDidCatch()
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Q1. How to upload the app to both stores
Ans. 
To upload the app to both stores, create separate builds for each platform and follow the submission guidelines of each store.
• Create separate builds for iOS and Android platforms

• Follow the submission guidelines of the App Store and Google Play Store

• Submit the builds to each store separately

• Ensure that the app meets the requirements of each store
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Q2. Local Storage in React-Native
Ans. 
Local storage in React Native allows data to be stored on the device for offline use.
• AsyncStorage is the built-in API for local storage in React Native

• Data is stored as key-value pairs

• Data can be retrieved and updated asynchronously

• Local storage can be used for caching data for offline use

• Example: AsyncStorage.setItem('key', 'value')

• Example: AsyncStorage.getItem('key')
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Q1. L1 vs L2 regularization
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L1 regularization adds penalty equivalent to absolute value of coefficients, promoting sparsity. L2 regularization adds penalty equivalent to square of coefficients, promoting smaller weights.
• L1 regularization is also known as Lasso regularization.

• L2 regularization is also known as Ridge regularization.

• L1 regularization can lead to feature selection by setting some coefficients to zero.

• L2 regularization is more stable ...read more
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Q2. Ensemble techniques
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Q1. How does a machine learning model acquire knowledge from training data, and what steps are involved in the model training pipeline?
Ans. 
Machine learning models learn from data through training, involving data preparation, model selection, training, and evaluation.
• Data Collection: Gather relevant data, e.g., images for image classification.

• Data Preprocessing: Clean and format data, e.g., normalizing pixel values.

• Model Selection: Choose an appropriate algorithm, e.g., decision trees or neural networks.

• Training: Feed data into the model to adjust weights,...read more
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Q2. What is the role of activation functions in neural networks, and how do they influence the learning process?
Ans. 
Activation functions introduce non-linearity, enabling neural networks to learn complex patterns and relationships in data.
• Activation functions determine the output of a neuron given an input, influencing the network's ability to model complex functions.

• Common activation functions include ReLU (Rectified Linear Unit), Sigmoid, and Tanh, each with unique properties.

• ReLU is popular for hidden layers due to its simplicity...read more
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