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I'm thrilled to say that I was selected in Amazon. There was a résumé shortlisting round, and after my résumé got selected, my interview was scheduled. # Interview Process There were two in-person interviews. ## 1. DSA Round This round focused on problem-solving and core data structures. I was asked two medium–hard level questions: One was a graph traversal question with a DP twist, which made it slightly tricky. The second was based on 1-D Dynamic Programming. ## 2. ML Breadth & Depth Round This round covered a wide range of Machine Learning concepts. Topics included: Self-attention mechanism Transformer architecture (encoder and decoder) Deep learning training process Classical ML algorithms Towards the end, I was also asked conditional questions on overfitting and underfitting, which tested conceptual clarity. Overall, I would rate the difficulty as medium to hard. # Advice for Others Practice 2–3 DSA questions daily and stay consistent. Build strong conceptual understanding of ML algorithms and architectures. Study the concepts of overfitting and underfitting in depth.
I'm thrilled to say that I was selected in Amazon. There was a résumé shortlisting round, and after my résumé got selected, my interview was scheduled. # Interview Process There were two in-person interviews. ## 1. DSA Round This round focused on problem-solving and core data structures. I was asked two medium–hard level questions: One was a graph traversal question with a DP twist, which made it slightly tricky. The second was based on 1-D Dynamic Programming. ## 2. ML Breadth & Depth Round This round covered a wide range of Machine Learning concepts. Topics included: Self-attention mechanism Transformer architecture (encoder and decoder) Deep learning training process Classical ML algorithms Towards the end, I was also asked conditional questions on overfitting and underfitting, which tested conceptual clarity. Overall, I would rate the difficulty as medium to hard. # Advice for Others Practice 2–3 DSA questions daily and stay consistent. Build strong conceptual understanding of ML algorithms and architectures. Study the concepts of overfitting and underfitting in depth.
## Interview Overview I attended the interview for the AI Internship Programme at the City Union Bank Centre of Excellence, SASTRA University. The interview was around 15 minutes long and focused mainly on my technical background, machine learning project, SQL knowledge, and basic banking concepts. The interview was conversational and the interviewer asked questions based on the skills and projects mentioned in my profile. This made it important to understand my projects properly rather than simply memorizing theoretical answers. ## My Project Discussion A major part of the interview was based on my Telecom Customer Churn Prediction project. I was asked to explain the project, the problem I was trying to solve, the dataset, preprocessing steps, and the machine learning models I used. I explained how I performed exploratory data analysis, identified missing values, handled categorical variables through encoding, prepared the data for modelling, and divided it into training and testing datasets. I had experimented with multiple machine learning algorithms, including: - Logistic Regression - Decision Tree - Random Forest I also discussed the evaluation metrics used to compare the models, including accuracy, precision, recall, and F1-score. One important takeaway from the project discussion was that you should be able to explain not only what model you used, but also WHY you used it and how you decided which model performed better. ## SQL and Technical Questions The interview also covered SQL and basic technical concepts. Revising SQL before the interview was useful, especially concepts such as filtering data, aggregation, grouping, joins, and writing queries to retrieve meaningful information from tables. The interview also tested my understanding of basic machine learning concepts rather than only asking me to write code. For students preparing for similar interviews, I would recommend being comfortable with: - Python fundamentals - Pandas and NumPy - SQL queries - Machine learning fundamentals - Data preprocessing - Classification algorithms - Model evaluation metrics - Basic statistics - Exploratory Data Analysis ## Banking Concepts Since the internship was related to banking and customer analytics, I was also expected to have an understanding of basic banking concepts. The preparation included concepts related to customer analysis, banking products, transactions, and how data analytics can be used to understand customer behaviour. Having a basic understanding of banking terminology is useful when applying for AI or Data Science roles in the BFSI domain. Technical knowledge alone may not be enough when the internship involves solving business problems using financial or customer data. ## What I Learned One of the biggest lessons from the interview was the importance of understanding your own resume. If you mention a machine learning project, be prepared to explain the complete workflow: 1. What problem are you solving? 2. What dataset did you use? 3. What preprocessing did you perform? 4. How did you handle missing values? 5. How did you encode categorical variables? 6. Which models did you try? 7. Why did you choose those models? 8. Which evaluation metrics did you use? 9. Which model performed best and why? 10. What could you improve in the future? The same applies to programming languages and tools mentioned in your resume. Interviewers can ask questions from any skill you list. ## Preparation Tips For students preparing for AI/ML internships, I would recommend focusing on fundamentals instead of trying to learn too many advanced topics at the last minute. Revise Python, SQL, machine learning algorithms, EDA, preprocessing, and evaluation metrics. At the same time, understand your projects deeply and practice explaining them in simple language. For banking-related AI roles, spend some time learning basic banking concepts and customer analytics terminology as well. Most importantly, don't just memorize definitions. Try to understand how a concept would actually be used to solve a real-world problem. ## Final Takeaway The interview was a good opportunity to connect my academic knowledge and machine learning project experience with a real-world banking use case. It also showed me that for internship interviews, strong fundamentals and the ability to clearly explain your own work are extremely important. If you are preparing for a similar AI/Data Science internship, focus on your projects, Python, SQL, ML fundamentals, and domain-specific concepts. Be honest about what you know, and make sure you can confidently explain everything mentioned on your resume.
## Interview Overview I attended the interview for the AI Internship Programme at the City Union Bank Centre of Excellence, SASTRA University. The interview was around 15 minutes long and focused mainly on my technical background, machine learning project, SQL knowledge, and basic banking concepts. The interview was conversational and the interviewer asked questions based on the skills and projects mentioned in my profile. This made it important to understand my projects properly rather than simply memorizing theoretical answers. ## My Project Discussion A major part of the interview was based on my Telecom Customer Churn Prediction project. I was asked to explain the project, the problem I was trying to solve, the dataset, preprocessing steps, and the machine learning models I used. I explained how I performed exploratory data analysis, identified missing values, handled categorical variables through encoding, prepared the data for modelling, and divided it into training and testing datasets. I had experimented with multiple machine learning algorithms, including: - Logistic Regression - Decision Tree - Random Forest I also discussed the evaluation metrics used to compare the models, including accuracy, precision, recall, and F1-score. One important takeaway from the project discussion was that you should be able to explain not only what model you used, but also WHY you used it and how you decided which model performed better. ## SQL and Technical Questions The interview also covered SQL and basic technical concepts. Revising SQL before the interview was useful, especially concepts such as filtering data, aggregation, grouping, joins, and writing queries to retrieve meaningful information from tables. The interview also tested my understanding of basic machine learning concepts rather than only asking me to write code. For students preparing for similar interviews, I would recommend being comfortable with: - Python fundamentals - Pandas and NumPy - SQL queries - Machine learning fundamentals - Data preprocessing - Classification algorithms - Model evaluation metrics - Basic statistics - Exploratory Data Analysis ## Banking Concepts Since the internship was related to banking and customer analytics, I was also expected to have an understanding of basic banking concepts. The preparation included concepts related to customer analysis, banking products, transactions, and how data analytics can be used to understand customer behaviour. Having a basic understanding of banking terminology is useful when applying for AI or Data Science roles in the BFSI domain. Technical knowledge alone may not be enough when the internship involves solving business problems using financial or customer data. ## What I Learned One of the biggest lessons from the interview was the importance of understanding your own resume. If you mention a machine learning project, be prepared to explain the complete workflow: 1. What problem are you solving? 2. What dataset did you use? 3. What preprocessing did you perform? 4. How did you handle missing values? 5. How did you encode categorical variables? 6. Which models did you try? 7. Why did you choose those models? 8. Which evaluation metrics did you use? 9. Which model performed best and why? 10. What could you improve in the future? The same applies to programming languages and tools mentioned in your resume. Interviewers can ask questions from any skill you list. ## Preparation Tips For students preparing for AI/ML internships, I would recommend focusing on fundamentals instead of trying to learn too many advanced topics at the last minute. Revise Python, SQL, machine learning algorithms, EDA, preprocessing, and evaluation metrics. At the same time, understand your projects deeply and practice explaining them in simple language. For banking-related AI roles, spend some time learning basic banking concepts and customer analytics terminology as well. Most importantly, don't just memorize definitions. Try to understand how a concept would actually be used to solve a real-world problem. ## Final Takeaway The interview was a good opportunity to connect my academic knowledge and machine learning project experience with a real-world banking use case. It also showed me that for internship interviews, strong fundamentals and the ability to clearly explain your own work are extremely important. If you are preparing for a similar AI/Data Science internship, focus on your projects, Python, SQL, ML fundamentals, and domain-specific concepts. Be honest about what you know, and make sure you can confidently explain everything mentioned on your resume.
# The selection process for Deloitte typically focuses on core data structures, algorithmic optimization, and database architecture. ## Selection Process: * PPT Presentation * Online Coding Test * Technical Interview 1/ 2/3 (Based on the interview performance) * HR Interview ## Recruiting Tips From developing a standout resume to putting your best foot forward in the interview, we want you to feel prepared and confident as you explore opportunities at Hashedin Technologies. Here are some recruiting tips from our Team * Be Prepared to discuss your approach to challenges and problem-solving, explaining your thought process and decision making. * Be ready to discuss personal projects in detail, highlighting your role and contributions to showcase your technical skills and communication abilities. * Research the company thoroughly to understand its values, mission, culture and recent developments, demonstrating your genuine interest. * During problem-solving or case study interviews, focus on demonstrating your unique approach and thought process by showcasing your innovative thinking. ## Round Details ## 1. Coding Round - **Number of Questions**: 3 Questions - **Breakdown**: 1 Easy, 2 Medium - **Key Problems**: - String search: Check if a given string is present in an array of strings. - Array Optimization: Find all pairs $(i, j)$ where $arr[i] > arr[j] * 3$. - *Note: Requires an optimized $O(n \log n)$ approach to pass large test cases.* ## 2. Technical Interview (One-to-One) - **Number of Questions**: 3–5 Questions - **Difficulty**: Medium - **Topics**: - **Heaps**: Implementation or priority queue applications. - **Two Pointers**: Used for array/string manipulation. - **Linked Lists**: Detecting cycles and finding the intersection point of two lists. ## 3. Database Design Round - **Focus**: System architecture and data modeling. - **Scenario**: Airport Booking System. - **Requirements**: - Draw/Explain ER Diagrams. - Perform Schema Design. - Write complex SQL queries based on the designed schema. --- ## **Tips** * **Communication is Key**: If you explain your logic confidently and correctly, interviewers may waive the requirement to write the full code for certain questions. * **Optimization**: For array pair problems, think beyond nested loops; consider modified Merge Sort or Fenwick tree logic for large constraints.
# The selection process for Deloitte typically focuses on core data structures, algorithmic optimization, and database architecture. ## Selection Process: * PPT Presentation * Online Coding Test * Technical Interview 1/ 2/3 (Based on the interview performance) * HR Interview ## Recruiting Tips From developing a standout resume to putting your best foot forward in the interview, we want you to feel prepared and confident as you explore opportunities at Hashedin Technologies. Here are some recruiting tips from our Team * Be Prepared to discuss your approach to challenges and problem-solving, explaining your thought process and decision making. * Be ready to discuss personal projects in detail, highlighting your role and contributions to showcase your technical skills and communication abilities. * Research the company thoroughly to understand its values, mission, culture and recent developments, demonstrating your genuine interest. * During problem-solving or case study interviews, focus on demonstrating your unique approach and thought process by showcasing your innovative thinking. ## Round Details ## 1. Coding Round - **Number of Questions**: 3 Questions - **Breakdown**: 1 Easy, 2 Medium - **Key Problems**: - String search: Check if a given string is present in an array of strings. - Array Optimization: Find all pairs $(i, j)$ where $arr[i] > arr[j] * 3$. - *Note: Requires an optimized $O(n \log n)$ approach to pass large test cases.* ## 2. Technical Interview (One-to-One) - **Number of Questions**: 3–5 Questions - **Difficulty**: Medium - **Topics**: - **Heaps**: Implementation or priority queue applications. - **Two Pointers**: Used for array/string manipulation. - **Linked Lists**: Detecting cycles and finding the intersection point of two lists. ## 3. Database Design Round - **Focus**: System architecture and data modeling. - **Scenario**: Airport Booking System. - **Requirements**: - Draw/Explain ER Diagrams. - Perform Schema Design. - Write complex SQL queries based on the designed schema. --- ## **Tips** * **Communication is Key**: If you explain your logic confidently and correctly, interviewers may waive the requirement to write the full code for certain questions. * **Optimization**: For array pair problems, think beyond nested loops; consider modified Merge Sort or Fenwick tree logic for large constraints.
The interview was technical and started with brief introductions. We then jumped straight into two Data Structures and Algorithms (DSA) questions. The first was based on binary search, specifically searching on answers. I managed to solve it partially but struggled with the edge cases. The second question was about finding the Lowest Common Ancestor (LCA) in a binary tree. I solved it, but my solution was a bit complex.
The interview focused on Data Structures and Algorithms. One question involved a medium-difficulty binary search problem where I had to efficiently search for an optimal solution within a range, but I struggled with some edge cases. The second question was a standard tree problem, specifically finding the Lowest Common Ancestor in a Binary Tree. While I solved it, I made it a bit more complex than necessary. You can find the exact wording of the questions I was asked in the dedicated section below.
Amazon is one of the best companies in the world, and it definitely has a great company culture. The environment seemed very focused and driven, with a strong emphasis on problem-solving and innovation.
My main advice would be to write clean, well-documented code and explain your thought process clearly. The interviewer specifically told me to improve my code writing and explanation skills. Even if you solve the problem, poor code quality or a lack of clear communication can be a reason for rejection. Focus on making your code easy to understand and articulate your reasoning behind each step. Good luck!
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