Are you gearing up for a data analytics interview at Amazon? As one of the leading tech giants, Amazon places a strong emphasis on data-driven decision-making, making data analytics roles crucial within the organization. To help you prepare, we’ve compiled a list of common data analytics interview questions you might encounter during your Amazon interview, along with expert answers to guide you toward success.
Table of Contents
Technical Questions
Question: Design Data warehousing for an e-commerce company.
Answer: Designing a data warehouse for an e-commerce company involves planning a centralized repository that consolidates data from various sources to support decision-making, analytics, and reporting. The design process includes several critical steps and considerations, from understanding business needs to choosing the right technology stack. Below is a structured approach to designing an efficient and scalable data warehouse for an e-commerce company:
Question: Explain the SQL window function.
Answer: SQL window functions allow for advanced calculations across sets of rows related to the current row within a query, without collapsing the result set into a single output. They use the OVER() clause to define the partitioning and ordering of data for computations like running totals, averages, and ranking. Key types include aggregation functions (e.g., SUM, AVG) when applied over a window, and ranking functions (e.g., ROW_NUMBER(), RANK(), DENSE_RANK()), providing powerful tools for data analysis while preserving row-level detail.
Question: Explain about the CTE’s.
Answer: Common Table Expressions (CTEs) are temporary result sets that can be referenced within a SELECT, INSERT, UPDATE, or DELETE statement. CTEs provide a way to create more readable and modular SQL queries by defining a named temporary result set that is defined within the execution scope of a single statement. They are especially useful for breaking down complex queries into simpler parts and for recursive queries.
Question: How can Numpy be used?
Answer: NumPy, which stands for Numerical Python, is a foundational package for numerical computing in Python. It provides support for arrays, matrices, and a large library of mathematical functions to operate on these data structures. Here’s how NumPy can be used:
- Array Operations: Perform efficient array operations for fast numerical computation. This includes element-wise operations, logical operations, and statistical operations.
- Mathematical Functions: Utilize a wide range of mathematical functions to perform calculations such as linear algebra, Fourier transforms, and statistics.
- Random Number Generation: Generate random numbers and perform random sampling for simulations or modeling applications.
- Data Analysis: Use as a base for data analysis and manipulation, especially when integrated with libraries like pandas for handling larger datasets.
- Machine Learning: Serve as the backbone for numerical computations in machine learning libraries, such as scikit-learn, TensorFlow, and PyTorch, by providing optimized and efficient array operations.
Question: What are triggers and when will use them?
Answer: Triggers are database mechanisms that automatically execute in response to specific events, such as data modifications (INSERT, UPDATE, DELETE). They are used for enforcing business rules, maintaining data integrity, synchronizing data across tables, and implementing auditing and logging actions. While powerful for automating complex database operations, triggers should be used carefully due to potential impacts on performance and complexity in debugging.
Question: What is data science?
Answer: Data Science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data. It combines elements of statistics, mathematics, computer science, and domain expertise to analyze and interpret complex data sets. The goal of data science is to uncover patterns, trends, and correlations that can inform decision-making, solve problems, and drive innovation in various industries. Techniques used in data science include data mining, machine learning, statistical analysis, data visualization, and predictive modeling, all aimed at extracting valuable insights from data to gain a deeper understanding of phenomena and make data-driven decisions.
Question: What are different hypothesis techniques?
Answer:
- Parametric Tests: Z-test for known population variance, t-test for unknown variance, ANOVA for comparing means.
- Non-parametric tests: Wilcoxon Rank-Sum, Kruskal-Wallis, Chi-Square for independence.
- Correlation Tests: Pearson, Spearman, and Kendall test for measuring relationships.
- Goodness-of-Fit: Chi-Square test for categorical data fitting.
- Regression Analysis: Linear and Logistic Regression for predictive modeling.
- Proportion Tests: Z-test and Chi-Square for comparing proportions.
Question: Which is the best machine model and describe it?
Answer: Several machine learning models are
- Random Forest: Ensemble of decision trees, robust to overfitting, great for classification and regression.
- Support Vector Machines (SVM): Effective in high-dimensional spaces, good for clear margin of separation, use different kernels.
- Gradient Boosting Machines (GBM): Builds trees sequentially, has high predictive accuracy, and handles complex patterns.
- Neural Networks (Deep Learning): Mimics human brain, powerful for complex patterns, best for large datasets.
- K-Nearest Neighbors (KNN): Simple and intuitive, no training phase, works well with small datasets
Question: What are overfitting and underfitting?
Answer:
Overfitting:
Definition: Occurs when a model learns the training data too well, capturing noise or random fluctuations in the data as if they are real patterns.
Characteristics:
Low training error but high test error.
The model is too complex and captures noise as a signal.
Underfitting:
Definition: Occurs when a model is too simple to capture the underlying structure of the data.
Characteristics:
High error on both training and test/validation data.
The model fails to capture patterns and relationships.
Question: What are regression models?
Answer:
Linear Regression:
- Simple linear regression
- Multiple linear regression
Logistic Regression:
- Binary logistic regression
- Multinomial logistic regression
Polynomial Regression:
- Fits a polynomial equation to the data
Ridge Regression:
- Adds a penalty term to the coefficients to avoid overfitting
Lasso Regression:
- Uses L1 regularization to perform variable selection and shrink coefficients
Question: Explain the difference between linear and logistic regression.
Answer:
Output:
- Linear regression predicts continuous values along a continuous scale.
- Logistic regression predicts the probability of an event occurring, usually between 0 and 1.
Target Variable:
- Linear regression is used for continuous target variables.
- Logistic regression is used for binary or ordinal categorical target variables.
Application:
- Linear regression is used for predicting values like house prices, stock prices, or temperature.
- Logistic regression is used for classification tasks like spam detection, disease diagnosis, or customer churn prediction.
Error Function:
- Linear regression commonly uses Mean Squared Error (MSE) as the loss function.
- Logistic regression commonly uses Log Loss (Cross-Entropy Loss) as the loss function.
SQL Questions
Question: What is SQL?
Answer: SQL (Structured Query Language) is a standardized programming language used to manage and manipulate relational databases. It is used for tasks such as querying data, inserting or updating records, and creating or modifying database structures.
Question: Explain the difference between SQL and NoSQL databases.
Answer:
SQL databases are relational databases that store data in tables and use structured schema. Examples include MySQL and PostgreSQL.
NoSQL databases are non-relational databases that store data in various ways, such as key-value pairs, documents, or graphs. Examples include MongoDB and Cassandra.
SQL databases are better suited for complex queries and transactions, while NoSQL databases are more flexible and scalable for handling large volumes of unstructured data.
Question: What is a JOIN in SQL?
Answer: A JOIN in SQL is used to combine rows from two or more tables based on a related column between them. Types of JOINs include:
- INNER JOIN: Returns rows when there is at least one match in both tables.
- LEFT JOIN: Returns all rows from the left table and matches rows from the right table.
- RIGHT JOIN: Returns all rows from the right table and matched rows from the left table.
- FULL (OUTER) JOIN: Returns rows when there is a match in either table.
Question: Explain the difference between GROUP BY and ORDER BY in SQL.
Answer:
GROUP BY is used to group rows that have the same values into summary rows. It is typically used with aggregate functions like SUM(), COUNT(), AVG(), etc.
ORDER BY is used to sort the result set in ascending or descending order based on one or more columns.
Question: What is a subquery in SQL?
Answer: A subquery is a query nested within another query. It can be used to return data that will be used in the main query as a condition, value, or table source. Subqueries can be used in SELECT, INSERT, UPDATE, and DELETE statements.
Question: Explain the difference between UNION and UNION ALL in SQL.
Answer:
UNION is used to combine the result sets of two or more SELECT statements into a single result set, removing duplicate rows.
UNION ALL also combines the result sets of two or more SELECT statements into a single result set, but it retains all rows, including duplicates.
Question: What is a primary key in SQL?
Answer: A primary key is a column or a set of columns that uniquely identifies each row in a table. It ensures that each row in the table is uniquely identifiable and cannot contain null or duplicate values.
Question: Explain the ACID properties of transactions in SQL.
Answer:
- Atomicity: Ensures that a transaction is treated as a single unit of work, either all of its operations are completed successfully or none of them are.
- Consistency: Ensures that a transaction brings the database from one consistent state to another, preserving data integrity.
- Isolation: Ensures that concurrent transactions do not interfere with each other, providing isolation between them.
- Durability: Ensures that once a transaction is committed, its changes are permanently saved in the database even in case of system failures.
Question: What is the difference between a view and a table in SQL?
Answer:
A table is a physical storage structure that stores data in rows and columns.
A view is a virtual table based on the result set of a SELECT query. It does not store data physically but provides a dynamic, up-to-date representation of the underlying tables.
Question: Explain the use of the HAVING clause in SQL.
Answer: The HAVING clause is used in conjunction with the GROUP BY clause to filter rows returned by a GROUP BY condition. It is used to apply a condition to groups of rows, similar to the WHERE clause which applies to individual rows.
Other Topics
SQL and data visualization questions.
SQL implementations with AWS and Cloud
SQL joins Basic Python questions
General Questions
Question: How did you analyze data?
Question: Who uses your data analysis results?
Question: How large is the data set you work with?
Question: Describe how you used Data to make a Decision.
Question: What are the top 5 software programs you are proficient in?
Question: Which functions in SQL do you like the most?
Question: Why did you apply for this role?
Question: What is one thing you like about yourself?
Question: What would you do when you don’t agree with others’ actions?
Question: Why do you choose Amazon?
Question: What is your goal in the future?
Question: How would you merge two tables?
Question: How to handle missing data with SQL?
Conclusion
Preparing for a data analytics interview at Amazon requires a solid understanding of fundamental concepts, hands-on experience with tools and techniques, and the ability to think analytically and problem-solve effectively. By familiarizing yourself with these common questions and crafting thoughtful answers, you can showcase your skills and expertise, setting yourself up for success in landing your dream job at Amazon’s dynamic and data-driven environment.
