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American Express Interview Questions and Answers
192 Interview questions
The ML model development lifecycle involves stages from problem definition to deployment and monitoring of the model.
1. Problem Definition: Identify the business problem and define objectives. Example: Predicting customer churn.
2. Data Collection: Gather relevant data from various sources. Example: Customer transaction records.
3. Data Preprocessing: Clean and prepare data for analysis. Example: Handling missing va...
Discount revenue recognition involves recording revenue after accounting for discounts offered to customers.
Revenue is recognized when earned, even if discounts are applied later.
For example, if a product sells for $100 with a $10 discount, revenue recognized is $90.
The accounting entry would debit Cash/Accounts Receivable and credit Sales Revenue.
Discounts can be classified as sales discounts or trade discounts, ...
The ROC curve plots true positive rate against false positive rate to evaluate classifier performance.
X-axis: False Positive Rate (FPR) - the ratio of negative instances incorrectly classified as positive.
Y-axis: True Positive Rate (TPR) - the ratio of positive instances correctly classified as positive.
Example: A model with a TPR of 0.9 and FPR of 0.1 indicates high sensitivity but some false alarms.
The ROC curve...
Random Forest is an ensemble method using bagging, while XGBoost uses boosting for improved accuracy and speed.
Random Forest builds multiple decision trees and merges them for better accuracy.
XGBoost optimizes the model by sequentially adding trees that correct errors of previous ones.
Random Forest is less prone to overfitting compared to individual decision trees.
XGBoost includes regularization techniques to prev...
Data imbalance can skew model performance; various techniques can help mitigate its effects.
Resampling techniques: Use oversampling (e.g., SMOTE) or undersampling to balance classes.
Use different evaluation metrics: Focus on precision, recall, or F1-score instead of accuracy.
Implement cost-sensitive learning: Assign higher misclassification costs to minority class errors.
Utilize ensemble methods: Techniques like R...
Merge sort is a divide-and-conquer algorithm that sorts an array by recursively splitting and merging sorted subarrays.
Divide: Split the array into two halves until each subarray contains a single element.
Conquer: Recursively sort each half of the array.
Combine: Merge the sorted halves back together to form a sorted array.
Example: Sorting [38, 27, 43, 3, 9, 82, 10] results in [3, 9, 10, 27, 38, 43, 82].
Time Comple...
Creating a diagram can effectively illustrate complex challenges faced in data engineering, such as data integration and scalability.
Data Integration Challenges: Illustrate how disparate data sources (e.g., SQL databases, NoSQL databases, APIs) were integrated into a unified data pipeline.
Scalability Issues: Show how the architecture evolved to handle increasing data volumes, such as transitioning from monolithic ...
To find a missing number in an array, use mathematical formulas or XOR operations for efficient solutions.
Sum Formula: For an array of size n with numbers from 1 to n+1, the missing number can be found using the formula: missing = (n+1)*(n+2)/2 - sum(array). Example: For [1, 2, 4], missing = 3.
XOR Method: XOR all numbers from 1 to n+1 and XOR all elements in the array. The result will be the missing number. Exampl...
Repartitioning in Spark optimizes data distribution across partitions, improving performance and resource utilization during processing.
Data Skew: Repartitioning helps to balance data across partitions, preventing some partitions from being overloaded while others are underutilized. For example, if one partition has 90% of the data, it can slow down processing.
Performance Optimization: By increasing or decreasing ...
Prioritizing a task backlog involves assessing value, effort, and alignment with strategic goals to ensure effective product development.
Value vs. Effort: I assess tasks based on their potential impact on user satisfaction versus the effort required. For example, a small feature that significantly enhances user experience may take priority over a larger, less impactful project.
Stakeholder Input: I regularly gather...
American Express Interview Experiences
386 interviews found
Anonymous
I applied via Naukri.com and was interviewed in Nov 2023. There was 1 interview round.
(3 Questions)
- Q1. Difference between query and path param in spring boot
- Ans.
Query param is used to pass parameters in the URL query string, while path param is used to define parameters in the URL path itself.
Query param is specified after the '?' in the URL, like '?param=value'
Path param is specified in the URL path itself, like '/{param}'
Query params are optional and can be used for filtering or sorting data
Path params are used to define variables in the URL path, like IDs or names
- Q2. Difference between array and array list
- Ans.
Arrays have fixed size, while ArrayLists can dynamically resize. ArrayLists are part of Java Collections framework.
Arrays have a fixed size, while ArrayLists can dynamically resize.
Arrays can hold primitive data types and objects, while ArrayLists can only hold objects.
Arrays use square brackets [] for declaration, while ArrayLists use angle brackets <>.
Arrays are faster than ArrayLists for accessing elements dir...
- Q3. Difference between abstract and interface
- Ans.
Abstract class can have both abstract and non-abstract methods, while interface can only have abstract methods.
Abstract class can have constructors, fields, and methods, while interface cannot have any of these.
A class can extend only one abstract class, but can implement multiple interfaces.
Abstract classes are used to provide a common base for subclasses, while interfaces are used to define a contract for classes to ...
Skills evaluated in this interview
I appeared for an interview in Apr 2025, where I was asked the following questions.
- Q1. Tell me about yourself?
- Q2. What are the GenAI industry trends? ChatGPT vs DeepSeek high level discussion.
- Ans.
GenAI trends focus on advancements in AI models, with ChatGPT and DeepSeek showcasing different applications and capabilities.
Increased adoption of conversational AI in customer service, exemplified by ChatGPT's integration into various platforms.
DeepSeek focuses on specialized knowledge retrieval, enhancing search capabilities in niche domains like legal or medical fields.
Emergence of hybrid models combining generativ...
- Q3. ML question: Random Forest vs XGBoost
- Ans.
Random Forest is an ensemble method using bagging, while XGBoost uses boosting for improved accuracy and speed.
Random Forest builds multiple decision trees and merges them for better accuracy.
XGBoost optimizes the model by sequentially adding trees that correct errors of previous ones.
Random Forest is less prone to overfitting compared to individual decision trees.
XGBoost includes regularization techniques to prevent o...
- Q4. ML question: Classification vs Regression
- Ans.
Classification predicts categories, while regression predicts continuous values in machine learning tasks.
Classification: Assigns labels to data points (e.g., spam vs. not spam).
Regression: Predicts numerical values (e.g., house prices based on features).
Classification algorithms include logistic regression, decision trees, and SVM.
Regression algorithms include linear regression, polynomial regression, and regression t...
- Q5. ML question: How does one handle data imbalance in a dataset?
- Ans.
Data imbalance can skew model performance; various techniques can help mitigate its effects.
Resampling techniques: Use oversampling (e.g., SMOTE) or undersampling to balance classes.
Use different evaluation metrics: Focus on precision, recall, or F1-score instead of accuracy.
Implement cost-sensitive learning: Assign higher misclassification costs to minority class errors.
Utilize ensemble methods: Techniques like Random...
- Q6. ML question: When to use accuracy, precision, recall? How to identify the classification model's performance using above three? F score, ROC area under the curve.
- Q7. ML question: What are the X and Y axis of ROC curve?
- Ans.
The ROC curve plots true positive rate against false positive rate to evaluate classifier performance.
X-axis: False Positive Rate (FPR) - the ratio of negative instances incorrectly classified as positive.
Y-axis: True Positive Rate (TPR) - the ratio of positive instances correctly classified as positive.
Example: A model with a TPR of 0.9 and FPR of 0.1 indicates high sensitivity but some false alarms.
The ROC curve help...
- Q8. SQL questions: Employee and department table; 1. Salary of employees with salary > avg salary 2. Salary of employees with salary > avg salary of department 3. Salary of top 3 employees with salary > avg sa...
- Q9. Any other questions for us?
- Ans.
I'm curious about the team's data science projects and how they align with the company's goals and vision.
What are the current data science projects the team is working on?
How does the data science team collaborate with other departments?
Can you share examples of how data-driven decisions have impacted the company?
What tools and technologies does the team primarily use?
How does the company support continuous learning a...
Interview Preparation Tips
(1 Question)
- Q1. Everything about Agile methodology - which method do you use, what is sprint velocity, how do you push it in the team, conflict resolution etc. Every question was theoretical - they didnt even ask about my...
(1 Question)
- Q1. Another round of basic scenario based questions.
Interview Preparation Tips
There were three coding questions.
(5 Questions)
- Q1. Could you provide a thorough explanation of the project?
- Q2. OOps Concepts and question from the technical subjects
- Q3. Why Spring Boot
- Q4. JWT Authentication
- Q5. Docker and Kubernetes
I appeared for an interview in Jan 2025.
(2 Questions)
- Q1. 4-5 SQL questions- Max price, categories in a retail store, no. of products bought per customer, joins, basic python
- Q2. Guesstimate: no. of amex credit card users in India. Follow up questions to test business acumen and understanding of the business
- Ans.
Estimating the number of American Express credit card users in India based on business acumen
Consider the market share of American Express in India compared to other credit card companies
Analyze the population size and income levels in India to estimate the potential number of credit card users
Look at the growth trends in the credit card industry in India to make a projection
Consider the marketing strategies and partne...
Interview Preparation Tips
Selected
I applied via Company Website and was interviewed in Aug 2024. There were 3 interview rounds.
(7 Questions)
- Q1. Work experience and relevance of your resume for Amex
- Ans.
My work experience includes roles in financial services and consulting, providing me with relevant skills for Amex.
I have worked in financial services for 5+ years, including roles at a major bank and a fintech startup.
I have experience in consulting, where I worked on projects related to financial analysis and strategy development.
My resume highlights my expertise in financial modeling, data analysis, and client relat...
- Q2. Why change to Amex? What attracted you the most about Amex?
- Ans.
I changed to Amex for its reputation for innovation, strong company culture, and opportunities for growth.
Reputation for innovation in the financial industry
Strong company culture focused on employee development and diversity
Opportunities for career growth and advancement within the company
- Q3. Technology questions such as how API works and your experience in working with data?
- Q4. How you used data management and analytics in your last role?
- Ans.
I utilized data management and analytics to track project progress, identify trends, and make data-driven decisions.
Implemented data management systems to organize and store project data efficiently
Utilized analytics tools to analyze project performance and identify areas for improvement
Generated reports and dashboards to track key metrics and communicate findings to stakeholders
Used data insights to make informed deci...
- Q5. How you can make business decisions from data in your last role?
- Ans.
I used data analysis tools to identify trends, patterns, and correlations to inform strategic business decisions.
Utilized data visualization tools to present key findings to stakeholders
Conducted regression analysis to predict future outcomes based on historical data
Collaborated with cross-functional teams to gather and analyze data from multiple sources
- Q6. How you solved any disagreement with your boss in last role?
- Ans.
I always approach disagreements with a focus on finding common ground and maintaining open communication.
Listen actively to understand the boss's perspective
Express my own perspective calmly and respectfully
Seek a compromise or solution that benefits both parties
Follow up to ensure the issue is fully resolved
- Q7. How will you approach different teams with requirements as a product manager?
- Ans.
I will approach different teams with requirements by understanding their unique needs, building relationships, and communicating effectively.
Understand the goals and priorities of each team to tailor requirements accordingly
Build relationships with key stakeholders to gain buy-in and support
Communicate clearly and regularly to ensure alignment and address any concerns
Collaborate with cross-functional teams to ensure al...
(4 Questions)
- Q1. Logical Questions - Check out Geeks for Geeks Puzzles and memorise them all or atleast understand logic for each
- Q2. 3 ants around a triangle problem
- Q3. Find top 3 horses from 25 horses set
- Ans.
To find the top 3 horses from a set of 25 horses, we can sort them based on their performance or rankings.
Sort the horses based on their performance or rankings
Select the top 3 horses from the sorted list
Consider factors like speed, endurance, and previous race results
- Q4. How will you approach conflicts at workplace?
- Ans.
I approach conflicts at workplace by actively listening, understanding all perspectives, and finding a mutually beneficial solution.
Actively listen to all parties involved in the conflict
Seek to understand the root cause of the conflict
Encourage open communication and dialogue
Find a mutually beneficial solution that addresses the concerns of all parties
Mediate if necessary to facilitate resolution
(4 Questions)
- Q1. Behavioural questions
- Q2. How will you manage work life balance?
- Ans.
I prioritize tasks, set boundaries, delegate when necessary, and make time for self-care.
Prioritize tasks based on deadlines and importance
Set boundaries by establishing work hours and sticking to them
Delegate tasks to team members when overwhelmed
Schedule time for self-care activities like exercise, hobbies, and relaxation
Communicate with supervisors and colleagues about workload and availability
- Q3. What are the 3 pillars of data management?
- Ans.
The 3 pillars of data management are data quality, data governance, and data security.
Data quality ensures that data is accurate, complete, and reliable.
Data governance involves establishing policies and procedures for managing data assets.
Data security focuses on protecting data from unauthorized access or breaches.
- Q4. How will you solve disputes and disagreements with your manager?
- Ans.
I will approach disputes with my manager by actively listening, seeking common ground, and proposing solutions collaboratively.
Actively listen to my manager's perspective and concerns
Seek common ground and areas of agreement
Propose solutions collaboratively, considering both perspectives
Maintain professionalism and respect throughout the discussion
Seek feedback and learn from the experience to prevent future conflicts
Interview Preparation Tips
Skills evaluated in this interview
(1 Question)
- Q1. Java, Hadoop, Pyspark Questions
(1 Question)
- Q1. Questions on current work experience
Data Science Analyst Interview Questions & Answers
Haifa Nisar Desai (B20BB013)
posted on 13 Jan 2025
(1 Question)
- Q1. Explain Backpropagation
- Ans.
Backpropagation is a method used in neural networks to update the weights by calculating the gradient of the loss function.
Backpropagation involves calculating the gradient of the loss function with respect to each weight in the network.
The gradient is then used to update the weights in the network in order to minimize the loss function.
This process is repeated iteratively until the network converges to a set of weight...
2 easy level dsa ques
(2 Questions)
- Q1. A simple 2d matrix ques
- Q2. Ques on computer networks and projects
(1 Question)
- Q1. How does Amex make money?
- Ans.
Amex makes money primarily through fees charged to merchants and cardholders, interest on outstanding balances, and membership fees.
Amex charges merchants a fee for accepting their cards, known as interchange fees.
Cardholders pay annual fees, late fees, and interest on balances carried over from month to month.
Amex also earns revenue from its network services, such as foreign exchange fees and travel-related services.
T...
Ashwarya Agarwal
Basic questions on mental ability and maths
Group discussion on generic debate topics
(3 Questions)
- Q1. About SQL Database
- Q2. About Puzzles and DBMS
- Q3. About Resume and projects
Top trending discussions
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