Blackcoffer Interview Questions, Process, and Tips

There was an assignment to be completed within the specified timeframe, focusing on natural language processing, which included various tasks such as web scraping, data cleaning, feature extraction, and other related activities. Following the completion of the assignment, there was a live coding and HR round that I found to be extremely challenging, especially for a fresher's role. The difficulty of the tasks assigned during the interview is quite high, even for experienced candidates. Overall, the interview process is very difficult to navigate; success can only come if you are exceptionally talented, very lucky, and possess a clear understanding of the concepts.

Q1. They first requested a thorough explanation of the assignment we completed, including a detailed code explanation and the logic employed.
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Q2. Live changes in the code of assignment are expected to be made during the Interview, such as adding a new feature or removing stop words, etc
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Q3. General situation-based HR questions were asked, and asked “how would you respond to the scenarios presented?”
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Q4. Questions based on technologies like AWS, GCP, Docker,etc
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An extremely challenging assignment was given with a very tight deadline. After submitting the assignment, an interview was scheduled during which numerous questions were asked about it, along with requests for complex modifications. Overall, the process was quite difficult.

Q1. General questions, work life balance, but tough questions in technical
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Basic simple easy aptitude questions

They gave 6 days for a Web Scrapping-NLP based assignment project to submit

Q1. Asked the approach to the assignment round
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Q2. How to handle imbalanced data in dataset
Ans. 
Handling imbalanced data involves techniques like resampling, using different algorithms, and adjusting class weights.
• Use resampling techniques like oversampling or undersampling to balance the dataset

• Utilize algorithms that are robust to imbalanced data, such as Random Forest, XGBoost, or SVM

• Adjust class weights in the model to give more importance to minority class
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Q3. While dynamic update of information in dataset to maintain the integrity and generalization of the data what measures will you use
Ans. 
To maintain data integrity and generalization, use techniques like data cleaning, normalization, and feature engineering.
• Perform data cleaning to remove errors, duplicates, and inconsistencies.

• Normalize data to ensure consistency and comparability.

• Utilize feature engineering to create new features or transform existing ones for better model performance.
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Q4. Different softwares for data analysis which i have used
Ans. 
I have used various software for data analysis including Python, R, SQL, Tableau, and Excel.
• Python - for data cleaning, manipulation, and modeling

• R - for statistical analysis and visualization

• SQL - for querying databases

• Tableau - for creating interactive visualizations

• Excel - for basic data analysis and visualization
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Make a dashboard using MERN/Django-React using custom data