Applied Cloud Computing Interview Questions, Process, and Tips

Q1. Array and string manipulation related questions all are basics
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Q2. Task related to your framework
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Q1. What are the projects you have worked on, and can you describe their architecture?
Ans. 
I have worked on projects involving e-commerce, social networking, and productivity apps with client-server architecture.
• Developed an e-commerce app with a client-side interface for users to browse products and a server-side database for inventory management.

• Built a social networking app with a client-server architecture for users to create profiles, connect with friends, and share updates.

• Designed a productivity app w...read more
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Q2. What are some basic questions regarding the tools you use to build projects?
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Q1. What deep questions related to your field were asked during the interview conducted by the tech lead?
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Basic questions from website codewar

Q1. Closures, promises
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Q2. Event loops, callback, task queue microtask queue
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Q3. Promises , differences between var let and const
Ans. 
Promises are used for asynchronous programming in JavaScript. var is function-scoped, let is block-scoped, and const is block-scoped and cannot be reassigned.
• Promises are used to handle asynchronous operations in JavaScript.

• var is function-scoped, let is block-scoped, and const is block-scoped and cannot be reassigned.

• Example: var x = 10; let y = 20; const z = 30;
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Q4. What are loops type of loops
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Types of loops in JavaScript include for, while, and do-while loops.
• For loop: Executes a block of code a specified number of times.

• While loop: Executes a block of code while a specified condition is true.

• Do-while loop: Executes a block of code once, and then repeats the loop as long as a specified condition is true.
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Q1. Project used technology related questions
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Q2. What are the in-depth questions related to the framework you mentioned?
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Q1. What is difference between random forest and xgboost
Ans. 
Random forest is an ensemble learning method using decision trees, while XGBoost is a gradient boosting algorithm.
• Random forest builds multiple decision trees and combines their predictions, while XGBoost builds trees sequentially to correct errors.

• Random forest is less prone to overfitting compared to XGBoost.

• XGBoost is computationally efficient and often outperforms random forest in terms of predictive accuracy.

• Rando...read more
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Q2. What is your project problem statement
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Q3. How you convince for clients the model is useful, what are the matrix
Ans. 
I convince clients of model usefulness by showcasing its accuracy, precision, recall, and F1 score.
• Explain the model's accuracy in predicting outcomes compared to actual results

• Discuss precision - the proportion of true positive predictions out of all positive predictions

• Highlight recall - the proportion of true positive predictions out of all actual positives

• Mention F1 score - the balance between precision and recall,
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Q1. How you take data from client
Ans. 
I collect data from clients through various methods such as surveys, interviews, API integrations, and data sharing agreements.
• Conduct surveys to gather specific data points from clients

• Interview clients to understand their data needs and preferences

• Integrate with client's systems through APIs to access real-time data

• Establish data sharing agreements to receive data from clients securely
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Q2. What are hyper parameters you turned for your model
Ans. 
Tuned hyperparameters include learning rate, batch size, number of layers, and activation functions.
• Adjusted learning rate to improve model convergence

• Optimized batch size for better training efficiency

• Experimented with different numbers of layers to find optimal model complexity

• Tried various activation functions to enhance model performance
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Q3. What difference model you use in your project
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Q4. How you work with llm
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Q5. What are the features in your data set
Ans. 
The features in the data set include age, gender, income, education level, location, and purchase history.
• Age

• Gender

• Income

• Education level

• Location

• Purchase history
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Q6. What feature engineering you did in project
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