In the competitive field of data science and analytics, landing a job at a leading tech company like Criteo can be a significant career milestone. Criteo, known for its data-driven advertising solutions, seeks candidates with strong analytical skills, a deep understanding of statistics, and proficiency in programming. To help you prepare, here are some common interview questions and answers that Criteo might ask, covering key areas such as SQL, dataset analysis, statistics, and Python.
Table of Contents
Dataset Analysis Interview Questions
Question: How do you handle missing data in a dataset?
Answer: Missing data can be handled by removing records with missing values, imputing missing values using methods like mean, median, or mode, or using algorithms that support missing values. The choice depends on the impact and proportion of missing data.
Question: What is the purpose of data normalization and how is it performed?
Answer: Data normalization scales numerical data to a common range, usually 0 to 1 or -1 to 1. It helps improve the performance of machine learning algorithms. Techniques include Min-Max scaling and Z-score standardization.
Question: What is the difference between classification and regression?
Answer: Classification predicts categorical labels (e.g., spam or not spam), while regression predicts continuous values (e.g., house prices). Both are types of supervised learning, but their outputs and evaluation metrics differ.
Question: How do you evaluate the performance of a machine learning model?
Answer: Model performance is evaluated using metrics like accuracy, precision, recall, F1-score for classification, and RMSE, MAE, R^2 for regression. Cross-validation helps in assessing the model’s generalizability.
Question: What is feature engineering and why is it important?
Answer: Feature engineering involves creating new features or transforming existing ones to improve model performance. It’s crucial as it directly impacts the model’s ability to learn patterns and make accurate predictions.
Question: Explain the concept of overfitting and how to prevent it.
Answer: Overfitting occurs when a model learns noise in the training data, performing well on training but poorly on unseen data. Prevention techniques include cross-validation, regularization, pruning (for decision trees), and using simpler models.
SQL Interview Questions
Question: How can you improve the performance of a slow query?
Answer: To improve query performance, you can use indexing, avoid SELECT *, limit the number of rows returned with WHERE clauses, use joins instead of subqueries, and optimize your database schema. Analyzing the query execution plan can also help identify bottlenecks.
Question: Explain the use of the window functions in SQL.
Answer: Window functions perform calculations across a set of table rows related to the current row without collapsing the result set. Examples include ROW_NUMBER(), RANK(), and SUM(). They are used with the OVER() clause to define the partitioning and ordering of the rows.
Question: What is a subquery and how is it used?
Answer: A subquery is a query nested inside another query. It can be used in SELECT, INSERT, UPDATE, or DELETE statements or inside another subquery. Subqueries help to break complex queries into simpler parts and can be used to filter, aggregate, or join data.
Question: How do you handle NULL values in SQL?
Answer: NULL values represent missing or unknown data. You can handle them using functions like COALESCE() to provide a default value, IS NULL or IS NOT NULL to filter them, and using IFNULL() or NVL() to replace them with another value in calculations.
Question: What is the difference between DELETE and TRUNCATE?
Answer: DELETE removes rows from a table based on a condition and can be rolled back, while TRUNCATE removes all rows from a table, cannot be rolled back, and resets any auto-increment counters. TRUNCATE is generally faster because it doesn’t generate individual row delete logs.
Question: Explain the concept of normalization.
Answer: Normalization is the process of organizing data to reduce redundancy and improve data integrity. It involves dividing large tables into smaller ones and defining relationships between them. The normal forms (1NF, 2NF, 3NF, etc.) guide this process to achieve database optimization.
Question: What is a primary key and how is it different from a unique key?
Answer: A primary key uniquely identifies each record in a table and cannot contain NULL values. A table can have only one primary key. A unique key also ensures all values are unique, but it can contain NULL values, and a table can have multiple unique keys.
Statistics Interview Questions
Question: What is the difference between correlation and causation?
Answer: Correlation measures the strength and direction of the relationship between two variables. Causation implies that one variable directly affects the other. Correlation does not imply causation; two variables can be correlated without one causing the other.
Question: Explain the concepts of Type I and Type II errors.
Answer: A Type I error occurs when the null hypothesis is incorrectly rejected (false positive). A Type II error occurs when the null hypothesis is not rejected when it is actually false (false negative). The significance level (alpha) controls the probability of a Type I error, while the power of a test relates to the probability of avoiding a Type II error.
Question: What is a confidence interval and how is it interpreted?
Answer: A confidence interval is a range of values derived from sample data that is likely to contain the population parameter with a specified level of confidence (e.g., 95%). If we say we have a 95% confidence interval, it means that if we were to take 100 different samples and compute a confidence interval for each, approximately 95 of the intervals will contain the population parameter.
Question: Explain the concept of statistical significance.
Answer: Statistical significance indicates that the observed effect in the data is unlikely to have occurred by chance, given a predefined significance level (alpha). If the p-value is less than the significance level, the result is considered statistically significant, suggesting a real effect or difference.
Question: What is multicollinearity and how can it be detected?
Answer: Multicollinearity occurs when independent variables in a regression model are highly correlated, leading to unreliable coefficient estimates. It can be detected using a Variance Inflation Factor (VIF) or by examining correlation matrices. High VIF values indicate multicollinearity.
Question: How do you choose an appropriate sample size for a study?
Answer: Choosing an appropriate sample size depends on factors like the desired confidence level, margin of error, population size, and expected effect size. Sample size calculators or power analyses can be used to determine the necessary sample size to achieve reliable and statistically significant results.
Question: What is a hypothesis test and how do you perform it?
Answer: A hypothesis test evaluates two mutually exclusive statements about a population to determine which statement is better supported by sample data. Steps include defining null and alternative hypotheses, choosing a significance level, calculating a test statistic, comparing it to a critical value or p-value, and making a decision to reject or not reject the null hypothesis.
Python Interview Questions
Question: What is a Python generator and how does it work?
Answer: A Python generator is a function that returns an iterator that yields values one at a time using the yield statement. Generators are memory efficient as they generate items on the fly and are useful for large datasets or streams of data.
Question: How do you handle exceptions in Python?
Answer: Exceptions in Python are handled using try-except blocks. Code that may raise an exception is placed in the try block, and the except block catches and handles the exception. Optional else and finally blocks can be used for additional logic and cleanup, respectively.
Question: What are Python’s built-in data types?
Answer: Python’s built-in data types include numeric types (int, float, complex), sequence types (list, tuple, range), text type (str), mapping type (dict), set types (set, frozenset), and boolean type (bool). Each type is suited for different kinds of operations and data handling.
Question: Explain the concept of list comprehension in Python.
Answer: List comprehension provides a concise way to create lists. It consists of brackets containing an expression followed by a for clause, and optionally, if clauses. For example, [x**2 for x in range(10) if x % 2 == 0] creates a list of squares of even numbers from 0 to 9.
Question: What is the difference between deepcopy and copy in Python?
Answer: The copy module’s copy function creates a shallow copy of an object, which copies the object but not the objects that it contains. The deepcopy function creates a deep copy, which recursively copies all objects, producing a fully independent clone of the original object.
Question: What is the Global Interpreter Lock (GIL) in Python?
Answer: The Global Interpreter Lock (GIL) is a mutex that protects access to Python objects, preventing multiple native threads from executing Python bytecodes simultaneously. This means that, in CPython, even with multi-threading, only one thread executes at a time, which can be a limitation for CPU-bound programs.
Question: How do you manage package dependencies in Python?
Answer: Package dependencies in Python are managed using tools like pip and virtualenv. pip is used to install and manage Python packages, while virtualenv creates isolated environments to ensure that dependencies for different projects do not conflict. Tools like pipenv and Poetry further streamline dependency management and environment creation.
Conclusion
Preparing for a data science and analytics interview at Criteo involves a strong grasp of SQL, dataset analysis, statistics, and Python programming. The questions outlined above provide a comprehensive guide to some of the key areas you should focus on. Understanding these concepts and practicing your answers will help you showcase your expertise and increase your chances of success in the interview. Good luck!
