Google Interview Questions

Alphabet (Google) uses advanced technology, analytics, and artificial intelligence to provide instant access to information through an extensive range of products and services, including YouTube, Cloud, and Android.

Given their high demand for data insights to drive business decisions, Google hires tech professionals who can handle a variety of roles, including developing models to improve Google’s search algorithms, voice recognition systems, and natural language processing (NLP) capabilities.

In this guide, we’ll cover a general overview of Google’s interview process, and then provide examples of common interview questions you may face across various topics.

Google’s interview process is designed to evaluate a candidate’s technical knowledge, critical thinking, and cultural fit. This multi-stage process generally includes:

1. Preliminary Screening: This initial step is conducted by a recruiter to get a sense of a candidate’s background and potential fit.
2. Technical Interviews: Several technical interviews (typically conducted over the phone) will be done to evaluate the depth of a candidate’s knowledge in areas like statistics, probability, basic coding, and machine learning algorithms.
3. Onsite Interview: Following successful technical interviews, candidates are invited for onsite interviews, which consist of 4-6 back-to-back sessions. Here, they face a mix of technical and behavioral questions. Often, the interviewers will present hypothetical business problems to see how a candidate determines the correct metrics, business approach, and how well they justify their methodology.

Google’s interview process can be quite demanding due to its focus on statistical and technical skills. It’s worth noting that interviews are typically conducted without a specific role in mind at first, with a team-matching process occurring after successful interviews.

Problem-solving is an essential skill for various tech roles at Google. Case study interview questions assess a candidate’s problem-solving abilities, analytical thinking, and application of technical knowledge to real-world scenarios.

1. How would you assess the validity of a 0.04 p-value from an AB test? 

Your company runs a standard control and variant AB test to increase conversion rates on the landing page, and the test yields a 0.04 p-value. Consider the factors behind conducting and measuring an AB test correctly.

1. What features would you add or change to improve Google Maps?

Describe what metrics you would use to measure the success of the improvements.

1. Determine if Youtube success is now limited to only “superstar” creators.

A Google product manager is concerned that amateur video creators aren’t able to achieve success on Youtube’s platform. What metrics or data would you assess to see if this is true?

1. How would you troubleshoot a system that has slowed down?

As a leader in large-scale systems, Google’s software must scale in proportion to its demand. What are the first steps to consider whenever a system slows down?

When practicing for case studies, we recommend using the product metrics and data analytics learning paths.

Statistics and probability are also common topics asked by Google. They are often included in data analyst, data scientist, and research scientist interviews.

1. What are the expected outcomes of ad ratings from two types of raters? 

In a scenario where ad ratings are performed by two types of raters (careful or lazy) with different probability patterns, calculate the expected number of good ads when:

• 100 raters each rate one ad independently
• One rater rates 100 ads

Additionally, find the probability that a lazy rater rated an ad given that it was rated as bad.

1. Determine the fairness of a coin based on the outcome of 10 flips. 

You flip a coin ten times, resulting in 8 tails and 2 heads. Based on this outcome, assess the fairness of the coin (i.e., p=0.5).

1. What is the distribution and expected value of a double-random function? 

You’re given a function that outputs a random integer between a minimum value, $N$, and a maximum value, $M$. If we use this function’s output as the max value in another random function with the same minimum value $N$, predict the distribution of the samples and the expected value.

To prepare for probability and statistics interview questions, check out the comprehensive probability learning path. It covers basic to advanced probability concepts, including multivariate distributions and sampling theorems.

Coding interview questions are commonly featured in data and software engineering positions at Google, as well as data scientist and other related roles. These questions typically cover data structures and algorithms, with a particular emphasis on tree and graph data structures. It’s important to know how to traverse, modify, and balance these data structures.

1. Write a function to merge two sorted lists into one. Given two sorted lists, write a function to merge them into one sorted list. What’s the time complexity?

2. Create a function can_shift to check if one string can be shifted to form another string. Given two strings A and B, write a function can_shift to determine if string A can be shifted a certain number of places to yield string B.

3. Develop an algorithm to move all disks from peg A to peg C following the Tower of Hanoi conditions. Given pegs A, B, C, and n disks, move all the pegs from peg A to peg C.

To practice coding interview questions, check out the Python learning path or the full list of Algorithms questions in our database.

SQL is the industry standard for managing and manipulating databases. As such, interview questions can delve into a range of topics, including writing and optimizing SQL queries, data normalization, transaction control language commands, and indexing. Since effective database querying is an essential skill, SQL questions are asked for many roles during the interview process.

1. How can you find the last transaction for each day? You have a bank_transactions table with columns that include transaction_value and the transaction’s date and time. Create a query that retrieves the last transaction for each day, including the transaction id, datetime, and amount.

2. Write a query to select the second-highest salary in the engineering department. Using the employees and departments tables, write a query to find the second-highest salary in the engineering department. If the highest salary is shared by more than one person, your query should find the next unique highest salary.

3. Determine the Top 3 highest employee salaries per department. Using the employees and departments tables, write a SQL query to find the Top 3 highest salaries per department, with an output that includes the employee’s full name and department. For departments with less than 3 employees, return the highest or Top 2 salaries.

For more help with SQL, try the SQL learning path and the full list of SQL questions and solutions in our interview questions database.

Machine learning is an ever-evolving field that is integral to leveraging data for decision-making, trend identification, and customized user experiences. These questions are typically asked in data analyst, data scientist, and product analyst interviews.

1. How would you justify the complexity of a neural network model to non-technical stakeholders?  You’re asked to build a model using a neural network to address a certain business problem. Justify this approach and explain the model’s predictions in a way that non-technical stakeholders can understand.

2. How are coefficients of logistic regression interpreted for categorical and boolean variables? Discuss the interpretation of coefficients in a logistic regression model, specifically for categorical and boolean variables.

3. What distinguishes Lasso from Ridge Regression? Outline the differences between these two common techniques in regularization.

To get ready for machine learning interview questions, we recommend reviewing the modeling and machine learning course.

Practice for the Google interview with these recently asked interview questions.

When analyzing various interview experiences, we noticed that Google’s interview questions heavily emphasize data structures and algorithms questions, specifically traversal algorithms. Many interviewees reported that Google likes tinkering with hard questions, so be sure to practice your algorithms.

Some of the key concepts that you need to know to ace your Google Interview include:

Breadth-First Search and Depth-First Search

Breadth-First Search (BFS) and Depth-First Search (DFS) are techniques for traversing and searching tree or graph data structures. Knowing them is critical to understanding a number of more complex algorithms.

• Breadth-First Search (BFS): BFS works by visiting all the nodes of a graph at the present “depth” before moving on to the nodes at the next depth level. It’s usually implemented using a queue data structure.
• Depth-First Search (DFS): DFS visits a node and then iterates on each of its neighbors. If a neighbor hasn’t been visited, the algorithm goes on to that neighbor and repeats the process until all nodes are visited. It’s often implemented using a stack or recursion.

Graph Traversal Algorithms

• BFS and DFS: As described above, these algorithms also apply to graphs.
• Dijkstra’s Algorithm: This is a weighted graph traversal algorithm used to find the shortest path from one node to all other nodes.
• A* Algorithm: A* is used in pathfinding and graph traversal, as it finds a path to the given goal node that has the smallest cost (usually in terms of distance).

Dynamic Programming

Dynamic programming is a method for solving complex problems by breaking them down into simpler subproblems. This approach seeks to solve each subproblem only once, saving its answer in a table (generally implemented as an array or some sort of map) to avoid redundant computations.

• Knapsack Problem: Given a set of items, each with a weight and a value, the goal is to determine the most valuable combination of items to include in a collection without exceeding a given weight limit. This classic problem can be tackled with both 0/1 Knapsack (items cannot be broken) and Fractional Knapsack (part of an item can be taken).
• Longest Common Subsequence (LCS): This problem involves finding the longest subsequence common to two sequences. Dynamic programming help reduce the time complexity of this problem.
• Fibonacci Sequence: Dynamic programming is often used to calculate the $n^{th}$ Fibonacci number efficiently.

If you want to learn more about algorithmic-style questions, check out our question bank, which contains an exhaustive list of interview questions from top companies across various industries.