Prepare for and practice interview questions from General Motors across topics like SQL, Takehome, Algorithms and more.

General Motors Interview Questions

General Motors Interview Guides

Machine Learning

Coefficients of Logistic Regression

Lasso vs Ridge

Bagging vs Boosting

FAQ Matching

Classification and Regression

Data Structures & Algorithms

Given an array and a target integer, write a function that returns the indices of two integers in the array that add up to the target integer.

Target Indices

Write a function that tests whether a string of brackets is balanced.

The Brackets Problem

This question requires the implementation of the Fibonacci sequence using three different methods: recursively, iteratively, and using memoization.

Implementing the Fibonacci Sequence in Three Different Methods

Given a string, write a function to determine if it is palindrome or not.

String Palindromes

Matrix Multiplication

Statistics

Describe linear regression to various audiences with different levels of knowledge.

Explaining Linear Regression to Different Audiences

What is the difference between Logistic and Linear Regression?

Linear vs Logistic Regression

Product Sense & Metrics

Losing Users

How would you present the performance of each subscription to an executive?

Analyzing Churn Behavior

Brainteasers

How would you answer when an Interviewer asks why you applied to their company?

Why Do You Want to Work With Us

What do you tell an interviewer when they ask you what your strengths and weaknesses are?

Your Strengths and Weaknesses

When an interviewer asks a question along the lines of:

<ul>
<li>What would your current manager say about you? What constructive criticisms might he give?</li>
<li>What are your three biggest strengths and weaknesses you have identified in yourself?</li>
</ul>

How would you respond?

When asked about your strengths in an interview, what is an effective way to respond?

When asked about your strengths in an interview, what is an effective way to respond?

Your Strengths and Weaknesses I

Which of the following is an acceptable strategy when discussing weaknesses in an interview?

Which of the following is an acceptable strategy when discussing weaknesses in an interview?

Your Strengths and Weaknesses II

When an interviewer asks you a question along the lines of:

<ul>
<li>Why did you apply to our company?</li>
<li>What are you looking for in your next job?</li>
<li>What makes you a good fit for our company?</li>
</ul>

How should you respond?

When asked 'What are you looking for in your next job?' in an interview, how can you tie the company's employee benefits into your response?

When asked 'What are you looking for in your next job?' in an interview, how can you tie the company's employee benefits into your response?

Why Do You Want to Work With Us I

How can company values be used effectively in an interview when asked 'What makes you a good fit for our company?'

How can company values be used effectively in an interview when asked 'What makes you a good fit for our company?'

Why Do You Want to Work With Us II

When responding to the question 'Why did you apply to our company?' during an interview, what aspect should you highlight?

When responding to the question 'Why did you apply to our company?' during an interview, what aspect should you highlight?

Why Do You Want to Work With Us III

Let’s say we’re comparing two machine learning algorithms. In which case would you use a bagging algorithm versus a boosting algorithm? 

Give an example of the tradeoffs between the two.

In machine learning, when would you use a bagging algorithm over a boosting algorithm?

In machine learning, when would you use a bagging algorithm over a boosting algorithm?

Bagging vs. Boosting

Given a string, write a function to determine if it is palindrome or not.

Note: A palindrome is a word/string that is read the same way forward as it is backward, e.g. <code>&#39;reviver&#39;</code>, <code>&#39;madam&#39;</code>, <code>&#39;deified&#39;</code> and <code>&#39;civic&#39;</code> are all palindromes, while <code>&#39;tree&#39;</code>, <code>&#39;music&#39;</code> and <code>&#39;person&#39;</code> are not palindromes.

Example:

Input:

<pre tabindex="0" class="chroma"><code>word1 = &#34;tree&#34;
word2 = &#34;radar&#34;
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>def is_palindrome(word1) -&gt; False
def is_palindrome(word2) -&gt; True
</code></pre>

What is the difference between Logistic and Linear Regression?

When would use one instead of the other in practice?

Given a <code>users</code> table, write a query to return only its duplicate rows.

Example:

Input:

<code>users</code> table

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>name</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>created_at</code></td>
<td>DATETIME</td>
</tr>
</tbody>
</table>

Duplicate Rows

What are the key differences between classification models and regression models?

What is the MOST important difference between regression and classification models?

What is the MOST important difference between regression and classification models?

Classification vs Regression

Given an array and a target integer, write a function <code>sum_pair_indices</code> that returns the indices of two integers in the array that add up to the target integer. If not found, just return an empty list.

Note: Can you do it on $O(n)$ time?

Note: Even though there could be many solutions, only one needs to be returned.

Example 1:

Input:

<pre tabindex="0" class="chroma"><code>array = [1 2 3 4] 
target = 5 
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>def sum_pair_indices(array, target) -&gt; [0 3] or [1 2]
</code></pre>

Example 2:

Input:

<pre tabindex="0" class="chroma"><code>array = [3]
target = 6 
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>Do NOT return [0 0] as you can&#39;t use an index twice.
</code></pre>

What’s the difference between Lasso and Ridge Regression?

Under what circumstances is it recommended to use Lasso regression over Ridge regression?

Under what circumstances is it recommended to use Lasso regression over Ridge regression?

The Fibonacci sequence is a series of numbers in which each number is the sum of the two preceding ones, usually starting with 0 and 1. It is often used in algorithm examples, and is defined by the following formula: F(n) = F(n-1) + F(n-2), with F(0) = 0 and F(1) = 1.

Your task is to implement the Fibonacci algorithm in three different methods:
1. Recursively
2. Iteratively
3. Using Memoization

Example 1:

Input:

<pre tabindex="0" class="chroma"><code>n = 5
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>fibonacci(n) -&gt; 5
</code></pre>

Example 2:

Input:

<pre tabindex="0" class="chroma"><code>n = 10
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>fibonacci(n) -&gt; 55
</code></pre>

The Fibonacci sequence starts as follows: 0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55…

You’re given a string that may contain the characters <code>{</code>, <code>}</code>, <code>[</code>, <code>]</code>, <code>(</code>, and <code>)</code>.

Task: Verify that the string is balanced. A balanced string is one where every opening character, <code>{</code>, <code>[</code>, or <code>(</code>, has a corresponding closing character, <code>}</code>, <code>]</code>, or <code>)</code>.

Write a function called <code>is_balanced(string: str) -&gt; bool</code> which verifies the balance of a string.

Example:

<pre tabindex="0" class="chroma"><code>is_balanced(&#39;(())[]{}&#39;) -&gt; True
</code></pre>

<pre tabindex="0" class="chroma"><code>is_balanced(&#39;{([(){}])()}&#39;) -&gt; True
</code></pre>

<pre tabindex="0" class="chroma"><code>is_balanced(&#39;{}[]())&#39;) -&gt; False
</code></pre>

<hr/>

How would you explain the concept of linear regression to three different audiences: a child, a first-year college student, and a seasoned mathematician? Ensure your explanations are tailored to the understanding level of each audience.

How would you interpret coefficients of logistic regression for categorical and boolean variables?

Why is one-hot encoding recommended for categorical variables in logistic regression models?

Why is one-hot encoding recommended for categorical variables in logistic regression models?

Let’s say that you work for a company like Netflix. Netflix has two pricing plans: $15/month or $100/year.

Let’s say an executive at the company wants you to analyze the churn behavior of users that subscribe to either plan.

What kinds of metrics / graphs / models would you build to help give the executive an over-arching view of how the subscriptions are performing?

Let’s say that we want to build a chatbot system for frequently asked questions. Whenever a user writes a question, we want to return the closest answer from a list of FAQs.

What are some machine learning methods for building this system?

Suppose that you work at a fintech startup. Recently, management has raised the issue of the increased amount of developer hours needed to implement relatively simple features, citing tech debt as the primary cause. How would you go around decreasing tech debt and decreasing developer turnaround time?

Prioritized debt reduction, process improvement, and a focus on maintainability for fintech efficiency

Decreasing Tech Debt

Analytics

Let’s say you are working at Facebook and are asked to investigate the claim that Facebook is losing young users.

<ol>
<li>How would you verify this claim?</li>
<li>What test metrics would you look at?</li>
</ol>

Let’s say you are working at Facebook and are asked to investigate the claim that Facebook is losing young users.

Pick the metric that is the least relevant for your analysis.

Let’s say you are working at Facebook and are asked to investigate the claim that Facebook is losing young users.

Pick the metric that is the least relevant for your analysis.

Let’s say we have a search form in our web app where users can enter a keyword like “star”, and the system searches for matches across several different fields in a large database table. Lately, these searches have been timing out when users search for common words.

How would you approach diagnosing and resolving these search timeouts?

How would you resolve slow, timing-out keyword searches across multiple database fields?

Search Timeout

Query Optimization

Given two matrices <code>A</code> and <code>B</code>, return their product matrix.

Matrix multiplication is defined only if the number of columns in matrix <code>A</code> is equal to the number of rows in matrix <code>B</code>.

You must compute the result without using any built-in matrix multiplication libraries.

<h3>Example 1</h3>

Input

<pre tabindex="0" class="chroma"><code>A = [[1,2],
 [3,4]]
B = [[5,6],
 [7,8]]
</code></pre>

Output

<pre tabindex="0" class="chroma"><code>def multiply(A, B) -&gt;
[[19,22],
 [43,50]]
</code></pre>

<h3>Example 2</h3>

Input

<pre tabindex="0" class="chroma"><code>A = [[1,0,2],
 [-1,3,1]]
B = [[3,1],
 [2,1],
 [1,0]]
</code></pre>

Output

<pre tabindex="0" class="chroma"><code>def multiply(A, B) -&gt;
[[5,1],
 [4,2]]
</code></pre>

<h3>Constraints</h3>

<ul>
<li><code>A</code> - <code>m x n</code> integer matrix</li>
<li><code>B</code> - <code>n x p</code> integer matrix</li>
<li><code>1 &lt;= m, n, p &lt;= 100</code></li>
<li><code>-100 &lt;= A[i][j], B[i][j] &lt;= 100</code></li>
<li><code>The input matrices are always valid for multiplication</code></li>
</ul>

Calculate Moving Average	SQL	Easy
Predict Customer Churn	Machine Learning	Medium
A/B Test Significance	Statistics	Medium
Optimize Query Performance	SQL	Hard
Feature Importance Analysis	Machine Learning	Medium
Clean Missing Data	Python	Easy
Neural Network Architecture	Deep Learning	Hard
Calculate Cohort Retention	SQL	Medium
Bayesian Probability	Statistics	Easy
Recommend Similar Products	Machine Learning	Hard

General Motors Interview Questions

General Motors Interview Guides

General Motors Interview Questions

Challenge

General Motors Salaries by Position

Discussion & Interview Experiences

Discussion & Interview Experiences