10+ Excitel Broadband Interview Questions and Answers

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, useful for imbalanced datasets

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

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.

• Random forest is easier to tune and less sensitive to hyperpara...read more

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

The features in the data set include age, gender, income, education level, location, and purchase history.

• Age

• Gender

• Income

• Education level

• Location

• Purchase history

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

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 certain criteria like correlation, importance scores, or domai...read more

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 a WHERE clause to specify which rows to delete, while tru...read more

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;

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.

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 model is too complex and captures noise in the training data i...read more

Migration of software involves transferring data, applications, and other components from one system to another.

• Assess current software and infrastructure

• Plan migration strategy

• Test migration process thoroughly

• Train users on new software

• Monitor performance post-migration