Applied Cloud Computing Interview Questions and Answers

Q1. Array and string manipulation related questions all are basics
Add your answer
Q2. Task related to your framework
Add your answer

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
Answered by AI
Add your answer
Q2. What are some basic questions regarding the tools you use to build projects?
Add your answer

Q1. What deep questions related to your field were asked during the interview conducted by the tech lead?
Add your answer

Basic questions from website codewar

Q1. Closures, promises
Add your answer
Q2. Event loops, callback, task queue microtask queue
Add your answer
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;
Answered by AI
Add your answer
Q4. What are loops type of loops
Ans. 
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.
Answered by AI
Add your answer

Q1. Project used technology related questions
Add your answer
Q2. What are the in-depth questions related to the framework you mentioned?
Add your answer

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
Answered by AI
Add your answer
Q2. What is your project problem statement
Add your answer
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,
Answered by AI
Add your answer

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
Answered by AI
Add your answer
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
Answered by AI
Add your answer
Q3. What difference model you use in your project
Add your answer
Q4. How you work with llm
Add your answer
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
Answered by AI
Add your answer
Q6. What feature engineering you did in project
Add your answer