In the rapidly evolving landscape of data science, interviews at leading financial institutions like CIBC (Canadian Imperial Bank of Commerce) can be both challenging and rewarding. Whether you’re a seasoned data scientist or just starting your career in this dynamic field, preparation is key to success. Here’s a comprehensive guide to the types of questions you might encounter in a data science interview at CIBC, along with tips on how to ace them.
Understanding the Landscape
Data science roles at CIBC typically involve leveraging data-driven insights to enhance business decisions, improve customer experiences, and optimize operational efficiencies. As such, interviewers are keen on assessing candidates’ technical proficiency, problem-solving abilities, and understanding of business applications of data science.
Table of Contents
SQL Interview Questions
Question: What are the different types of SQL statements?
Answer: SQL statements can be categorized into several types:
- DML (Data Manipulation Language): SELECT, INSERT, UPDATE, DELETE
- DDL (Data Definition Language): CREATE, ALTER, DROP, TRUNCATE
- DCL (Data Control Language): GRANT, REVOKE
- TCL (Transaction Control Language): COMMIT, ROLLBACK, SAVEPOINT
Question: What is a Primary Key?
Answer: A Primary Key is a column or a combination of columns that uniquely identifies each row in a table. It must contain unique values and cannot contain NULLs.
Question: What is a Foreign Key?
Answer: A Foreign Key is a column or a combination of columns that establishes a link between data in two tables. It ensures referential integrity by referencing the Primary Key in another table.
Question: What is a JOIN?
Answer: A JOIN clause is used to combine rows from two or more tables based on a related column between them. Types of JOINs include INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL OUTER JOIN.
Question: What is the difference between INNER JOIN and OUTER JOIN?
Answer:
- INNER JOIN: Returns only the rows that have matching values in both tables.
- OUTER JOIN: Returns all rows from one table and the matched rows from the second table. If there is no match, NULL values are returned for columns from the second table. Types include LEFT JOIN, RIGHT JOIN and FULL OUTER JOIN.
Question: What is a Subquery?
Answer: A Subquery is a query nested inside another query. It can be used in SELECT, INSERT, UPDATE, or DELETE statements to perform complex queries.
Question: What is a View?
Answer: A View is a virtual table based on the result set of an SQL query. It can encapsulate complex queries and present the data as a single table, which can simplify data access and improve security.
Question: What is an Index?
Answer: An Index is a database object that improves the speed of data retrieval operations on a table at the cost of additional space and potential performance overhead during data modification operations. Types include clustered and non-clustered indexes.
Question: What is Normalization?
Answer: Normalization is the process of organizing data in a database to minimize redundancy and improve data integrity. It involves dividing a database into two or more tables and defining relationships between them. Common normal forms include 1NF, 2NF, 3NF, and BCNF.
Python (Pandas, Numpy, Dictionary) Interview Questions
Question: What is a DataFrame in Pandas? Question: What is a DataFrame in Pandas?
Answer: A DataFrame is a two-dimensional, size-mutable, and potentially heterogeneous tabular data structure with labeled axes (rows and columns). It is similar to a table in a relational database or an Excel spreadsheet.
Question: How do you handle missing data in a Pandas DataFrame? Question: How do you handle missing data in a Pandas DataFrame?
Answer: Missing data can be handled using methods like dropna() to remove missing values, fillna() to fill missing values with a specified value, or interpolate() to estimate missing values based on other data points.
Question: How can you merge two DataFrames in Pandas? Question: How can you merge two DataFrames in Pandas?
Answer: You can merge two DataFrames using the merge() function, which allows you to specify the columns to join on and the type of join (inner, outer, left, or right). Another option is to use concat() to concatenate DataFrames along a particular axis.
Question: What is a Numpy array, and how is it different from a Python list? Question: What is a Numpy array, and how is it different from a Python list?
Answer: A Numpy array is a grid of values, all of the same type, indexed by a tuple of non-negative integers. It is more efficient for numerical operations compared to a Python list because it provides better performance, optimized memory usage, and a range of mathematical functions.
Question: How do you create a Numpy array? Question: How do you create a Numpy array?
Answer: Numpy arrays can be created using functions like array(), zeros(), ones(), arange(), and linspace(). For example, np.array([1, 2, 3]) creates a 1D array, and np.zeros((2, 3)) creates a 2D array of zeros.
Question: What is broadcasting in Numpy? Question: What is broadcasting in Numpy?
Answer: Broadcasting is a feature that allows Numpy to perform arithmetic operations on arrays of different shapes. It automatically expands the smaller array to match the shape of the larger array, enabling element-wise operations without creating redundant copies.
Question: How do you perform element-wise operations on Numpy arrays? Question: How do you perform element-wise operations on Numpy arrays?
Answer: Element-wise operations can be performed directly using arithmetic operators (+, -, *, /) or Numpy functions (np.add(), np.subtract(), np.multiply(), np.divide()). These operations are applied to corresponding elements of the arrays.
Question: How do you add and remove elements in a dictionary? Question: How do you add and remove elements in a dictionary?
Answer: To add an element, assign a value to a new key: dict[key] = value. To remove an element, use the del statement (del dict[key]) or the pop() method (dict.pop(key)).
Question: How can you iterate over the keys and values of a dictionary? Question: How can you iterate over the keys and values of a dictionary?
Answer: You can iterate over keys using for key in dict, over values using for value in dict.values(), and over key-value pairs using for key, value in dict.items().
Question: What are some common dictionary methods? Question: What are some common dictionary methods?
Answer: Common dictionary methods include get(key, default) to retrieve a value, keys() to get a list of keys, values() to get a list of values, items() to get a list of key-value pairs, update() to merge another dictionary, and clear() to remove all elements.
Question: How do you check if a key exists in a dictionary? Question: How do you check if a key exists in a dictionary?
Answer: You can check if a key exists using the in keyword: if key in dict:. This returns True if the key is present and False otherwise.
Collaborative filtering of Interview Questions
Question: What is collaborative filtering?
Answer: Collaborative filtering is a method used in recommender systems to predict a user’s interests by collecting preferences or behaviors from many users.
Question: Explain the difference between user-based and item-based collaborative filtering.
Answer: User-based collaborative filtering recommends items based on similarity between users. Item-based collaborative filtering recommends items based on the similarity between the items themselves.
Question: How do you handle the cold start problem in collaborative filtering?
Answer: The cold start problem occurs when there isn’t enough data about new users or items. Techniques to address this include using hybrid approaches (combining collaborative and content-based methods) or using popular items until enough data is gathered.
Question: What are some common similarity measures used in collaborative filtering?
Answer: Common similarity measures include cosine similarity, Pearson correlation, and Jaccard similarity. These measures help quantify the similarity between users or items based on their preferences or features.
Question: How can you evaluate the performance of a collaborative filtering algorithm?
Answer: Performance can be evaluated using metrics such as precision, recall, and mean average precision (MAP). Cross-validation techniques like k-fold validation can also assess how well the model generalizes to unseen data.
Question: Describe a scenario where collaborative filtering might not perform well.
Answer: Collaborative filtering may struggle in scenarios with sparse data, such as new or niche products with limited user interactions. It can also be less effective when there isn’t enough diversity in user preferences.
Question: What are some challenges of deploying collaborative filtering in a real-world application?
Answer: Challenges include handling scalability with large datasets, ensuring robustness against data noise or biases, and addressing privacy concerns related to user data.
Data Structure Interview Question
Question: What are the differences between an array and a linked list?
Answer: An array stores elements of the same data type in contiguous memory locations, allowing random access to elements using indexing. A linked list, on the other hand, consists of nodes where each node contains data and a reference (or pointer) to the next node, allowing dynamic memory allocation and efficient insertion/deletion operations.
Question: Explain the concept of a stack and give an example of its application.
Answer: A stack is a Last In, First Out (LIFO) data structure where elements are added and removed from the top. It supports two primary operations: push (adds an element to the top) and pop (removes the top element). An example application is the undo feature in text editors, where each action is pushed onto a stack and can be undone by popping from the stack.
Question: What is a binary search tree (BST)? How does it differ from other binary trees?
Answer: A binary search tree is a binary tree where the left subtree of a node contains only nodes with values less than the node’s value, and the right subtree contains only nodes with values greater than the node’s value. This property allows efficient searching, insertion, and deletion operations. Unlike other binary trees, BST maintains an ordering property that enables faster search operations.
Question: Describe the concept of hashing. How is it useful in data structures?
Answer: Hashing is a technique that maps data of arbitrary size to fixed-size values (hash codes) using a hash function. It is useful for storing and retrieving data in constant time O(1) on average, making it efficient for applications like implementing hash tables where quick lookup, insertion, and deletion operations are required.
Question: What are the advantages of using a linked list over an array?
Answer: Linked lists offer dynamic memory allocation, which allows efficient insertion and deletion of elements without needing contiguous memory. They also accommodate varying data sizes and can expand or shrink as needed. Additionally, linked lists do not suffer from the fixed size limitation of arrays and can be more memory-efficient when managing sparse data.
Question: How do you detect a cycle in a linked list?
Answer: To detect a cycle in a linked list, you can use Floyd’s cycle detection algorithm (also known as the tortoise and hare algorithm). This algorithm involves using two pointers (slow and fast) that traverse the linked list at different speeds. If there is a cycle, the two-pointers will eventually meet.
Conclusion
A data science interview at CIBC is not just about technical prowess but also about your ability to apply data-driven solutions to real-world challenges. By preparing thoroughly, understanding the company’s expectations, and showcasing your skills and experiences effectively, you can position yourself as a strong candidate for a rewarding career in data science at CIBC or similar institutions.
Remember, each interview experience is a valuable learning opportunity, regardless of the outcome. Stay confident, stay curious, and keep honing your skills to excel in the dynamic field of data science.
