Applied Cloud Computing Interview Questions, Process, and Tips for Experienced

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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Q1. Tell about yourself and family background.
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Q2. What process you follow
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Design exercise for admin

Q1. Explain bias variance tradeoff?
Ans. 
Bias-variance tradeoff is the balance between underfitting and overfitting in machine learning models.
• Bias refers to the error introduced by approximating a real-world problem, variance refers to the error introduced by modeling the noise in the training data.

• High bias can cause underfitting, where the model is too simple to capture the underlying patterns in the data.

• High variance can cause overfitting, where the mode...read more
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Q2. Explain PCA and feature selection technique ?
Ans. 
PCA is a dimensionality reduction technique that transforms data into a lower-dimensional space. Feature selection is the process of selecting a subset of relevant features for use in model training.
• PCA helps in reducing the dimensionality of data by finding the principal components that explain the most variance in the data.

• Feature selection involves selecting the most important features from the dataset based on cert...read more
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Q3. Explain difference between delete and truncate?
Ans. 
Delete removes rows one by one, while truncate removes all rows at once.
• Delete is a DML command and can be rolled back, while truncate is a DDL command and cannot be rolled back.

• Delete triggers delete triggers and fires delete triggers, while truncate does not trigger any triggers.

• Delete is slower as it logs individual row deletions, while truncate is faster as it logs the deallocation of the data pages.

• Delete can have...read more
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Q4. Explain your project in details?
Ans. 
Developed a machine learning model to predict customer churn in a telecom company.
• Used historical customer data to train the model

• Features included customer demographics, usage patterns, and customer service interactions

• Implemented a random forest algorithm for prediction

• Achieved an accuracy of 85% on test data
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Q5. How you have deployed your model and how to monitor the deployed model?
Ans. 
I have deployed models using cloud services like AWS SageMaker and monitored them using tools like Prometheus and Grafana.
• Deployed models using AWS SageMaker for easy scalability and management

• Utilized Prometheus and Grafana for monitoring model performance and health

• Set up alerts for abnormal behavior or performance degradation

• Regularly reviewed logs and metrics to ensure model is functioning as expected
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Implement algorithm from scratch