Prepare for and practice interview questions from Sam's Club across topics like Probability, Algorithms, Takehome and more.

Sam's Club Interview Questions

Sam's Club Interview Guides

Machine Learning

Interpret coefficients of logistic regression for categorical and boolean variables

Coefficients of Logistic Regression

Which model would perform better and why?

Booking Regression

How do we give each rejected applicant a reason why they got rejected?

Rejection Reason

How would you build a job recommendation feed?

Job Recommendation

Lasso vs Ridge

Select the 2nd highest salary in the engineering department

2nd Highest Salary

Write a query that returns all neighborhoods that have 0 users.

Empty Neighborhoods

Write a query to identify customers who placed more than three transactions each in both 2019 and 2020

Customer Orders

Last Transaction

Flight Records

Data Structures & Algorithms

Write a function to return a dataframe containing every transaction with a total value of over $100.

Over 100 Dollars

Given an integer N, write a function that returns all of the prime numbers up to N

Prime to N

Reconstruct the path of a trip so that the trip tickets are in order.

Scrambled Tickets

Given a list of strings, write a function that returns the longest common prefix

Common Prefix

Write a function to return True if two strings are anagrams of each other and False if they are not

Valid Anagram

Product Sense & Metrics

How would you measure the success of Facebook Groups

Group Success

How would you measure the success of the Instagram TV product

Instagram TV Success

Unified Inbox

How would you measure the success of Uber Eats?

Uber Eats Success

What are the top five metrics you would start tracking to understand the health of Google Docs

Docs Metrics

A/B Testing

How would you determine if the new model predicts delivery times better than the old model

Delivery Estimate Model

Button AB Test

How would you make a control group and test group to account for network effects

Network Experiment Design

How would you design a two-week A/B test to determine whether a pricing increase is a good business decision

Testing Price Increase

How would you evaluate the results and if the test was successful or not

Free Shipping Mention Test

<p>You are given a dictionary with two keys <code>a</code> and <code>b</code> that hold integers as their values.</p>

<p>Without declaring any other variable, swap the value of <code>a</code> with the value of <code>b</code> and vice versa.</p>

<p><em>Note: Return the dictionary after editing it.</em></p>

<p><strong>Example:</strong></p>

<p><strong>Input:</strong></p>

<pre tabindex="0" class="chroma"><code><span class="line"><span class="cl"><span class="n">numbers</span> <span class="o">=</span> <span class="p">{</span>
</span></span><span class="line"><span class="cl">  <span class="s1">&#39;a&#39;</span><span class="p">:</span><span class="mi">3</span><span class="p">,</span>
</span></span><span class="line"><span class="cl">  <span class="s1">&#39;b&#39;</span><span class="p">:</span><span class="mi">4</span>
</span></span><span class="line"><span class="cl"><span class="p">}</span>
</span></span></code></pre>

<p><strong>Output:</strong></p>

<pre tabindex="0" class="chroma"><code><span class="line"><span class="cl"><span class="k">def</span> <span class="nf">swap_values</span><span class="p">(</span><span class="n">numbers</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="p">{</span><span class="s1">&#39;a&#39;</span><span class="p">:</span><span class="mi">4</span><span class="p">,</span><span class="s1">&#39;b&#39;</span><span class="p">:</span><span class="mi">3</span><span class="p">}</span>
</span></span></code></pre>


Swap the values of `a` and `b` in the dictionary without declaring any other variable

Swap Variables

<p>Let’s say that you’re training a classification model.</p>

<p>How would you combat overfitting when building tree-based models?</p>


Let's say that you're training a classification model.   How would you combat overfitting when building tree-based models?

Overfit Avoidance

<p>Write a function to get a sample from a standard normal distribution.</p>


Write a function to get a sample from a standard normal distribution.

Normal Distribution Sample

Probability

<p>What is the difference between Logistic and Linear Regression?</p>

<p>When would use one instead of the other in practice?</p>


What is the difference between Logistic and Linear Regression?

Linear vs Logistic Regression

<p>We’re given two tables, a <code>users</code> table with demographic information and the neighborhood they live in and a <code>neighborhoods</code> table.</p>

<p>Write a query that returns all neighborhoods that have 0 users. </p>

<p><strong>Example:</strong></p>

<p><strong>Input:</strong></p>

<p><code>users</code> table</p>

<table>
<thead>
<tr>
<th>Columns</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>neighborhood_id</code></td>
<td>INTEGER</td>
</tr>

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

<table>
<thead>
<tr>
<th>Columns</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>city_id</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
<p><strong>Output:</strong></p>

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

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


<p>Write a query to identify customers who placed more than three transactions each in both 2019 and 2020.</p>

<p><strong>Example:</strong></p>

<p><strong>Input:</strong></p>

<p><code>transactions</code> table</p>

<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>user_id</code></td>
<td>INTEGER</td>
</tr>

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

<tr>
<td><code>product_id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>quantity</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
<p><code>users</code> table</p>

<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>
</tbody>
</table>
<p> 
<strong>Output:</strong></p>

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

<tbody>
<tr>
<td><code>customer_name</code></td>
<td>VARCHAR</td>
</tr>
</tbody>
</table>


<p>Which of the following queries will return a list of customers and the total amount they have spent on orders?</p>


Which of the following queries will return a list of customers and the total amount they have spent on orders?

<p>Write a SQL query to select the 2nd highest salary in the engineering department.</p>

<p><strong>Note:</strong> If more than one person shares the highest salary, the query should select the next highest salary.</p>

<p><strong>Example:</strong></p>

<p><strong>Input:</strong></p>

<p><code>employees</code> table</p>

<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>first_name</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>last_name</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>salary</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>department_id</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
<p><code>departments</code> table</p>

<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>
</tbody>
</table>
<p><strong>Output:</strong></p>

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

<tbody>
<tr>
<td><code>salary</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>


<p>Write a query to show the number of users, number of transactions placed, and total order amount per month in the year 2020. Assume that we are only interested in the monthly reports for a single year (January-December).</p>

<p><strong>Example:</strong></p>

<p><strong>Input:</strong></p>

<p><code>transactions</code> table</p>

<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>user_id</code></td>
<td>INTEGER</td>
</tr>

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

<tr>
<td><code>product_id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>quantity</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
<p><code>products</code> table</p>

<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>price</code></td>
<td>FLOAT</td>
</tr>
</tbody>
</table>
<p><code>users</code> table</p>

<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>sex</code></td>
<td>VARCHAR</td>
</tr>
</tbody>
</table>
<p><strong>Output:</strong></p>

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

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

<tr>
<td><code>num_customers</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>num_orders</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>order_amt</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>


Write a query to show the number of users, transactions, and total order amount per month in 2020

Monthly Customer Report

<p>A team wants to A/B test multiple different changes through a sign-up funnel.</p>

<p>For example, on a page, a button is currently red and at the top of the page. They want to see if changing a button from red to blue and/or from the top of the page to the bottom of the page will increase click-through.</p>

<p>How would you set up this test?</p>


<p>Your team wants to run an AB test on the color (red or blue) and position (top/bottom of the page) of a button that links to a promotion.</p>

<p>How many different variants of the test should you run to see the effect that a red button has on the bottom of the page, if currently the button is blue and on the top of the page.</p>


Your team wants to run an AB test on the color (red or blue) and position (top/bottom of the page) of a button that links to a promotion.

How many different variants of the test should you run to see the effect that a red button has on the bottom of the page, if currently the button is blue and on the top of the page.

A/B Test Variants

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

<p>Give an example of the tradeoffs between the two.</p>


<p>In machine learning, when would you use a bagging algorithm over a boosting algorithm?</p>


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

Bagging vs. Boosting

In which case would you use a bagging algorithm versus a boosting algorithm

Bagging vs Boosting

<p>Given a table of bank transactions with columns <code>id</code>, <code>transaction_value</code>, and <code>created_at</code> representing the date and time for each transaction, write a query to get the last transaction for each day.</p>

<p>The output should include the id of the transaction, datetime of the transaction, and the transaction amount. Order the transactions by datetime.</p>

<p><strong>Example:</strong></p>

<p><strong>Input:</strong></p>

<p><code>bank_transactions</code> table</p>

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

<tr>
<td><code>transaction_value</code></td>
<td>FLOAT</td>
</tr>
</tbody>
</table>
<p><strong>Output:</strong></p>

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

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

<tr>
<td><code>transaction_value</code></td>
<td>FLOAT</td>
</tr>

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


<p>Let’s say we have a table with an <code>id</code> and <code>name</code> fields. The table holds over 100 million rows and we want to sample a random row in the table without throttling the database.</p>

<p>Write a query to randomly sample a row from this table.</p>

<p><strong>Input:</strong></p>

<p><code>big_table</code> table</p>

<table>
<thead>
<tr>
<th>Columns</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>
</tbody>
</table>


Write a query to randomly sample a row from a big table.

Random SQL Sample

<p>Write a query to identify the manager with the biggest team size.</p>

<p>You may assume there is only one manager with the largest team size.</p>

<p><strong>Example:</strong></p>

<p><strong>Input:</strong></p>

<p><code>employees</code> table</p>

<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>manager_id</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
<p><code>managers</code> table</p>

<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>team</code></td>
<td>VARCHAR</td>
</tr>
</tbody>
</table>
<p><strong>Output:</strong></p>

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

<tbody>
<tr>
<td><code>manager</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>team_size</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>


Manager Team Sizes

<p>The probability that it will rain tomorrow is dependent on whether or not it is raining today and whether or not it rained yesterday.</p>

<p>If it rained yesterday and today, there is a 20% chance it will rain tomorrow. If it rained one of the days, there is a 60% chance it will rain tomorrow. If it rained neither today nor yesterday, there is a 20% chance it will rain tomorrow.</p>

<p>Given that it is raining today and that it rained yesterday, write a function <code>rain_days</code> to calculate the probability that it will rain on the nth day after today.</p>

<p><strong>Example:</strong></p>

<p><strong>Input:</strong></p>

<pre tabindex="0" class="chroma"><code><span class="line"><span class="cl"><span class="n">n</span><span class="o">=</span><span class="mi">5</span>
</span></span></code></pre>

<p><strong>Output:</strong></p>

<pre tabindex="0" class="chroma"><code><span class="line"><span class="cl"><span class="k">def</span> <span class="nf">rain_days</span><span class="p">(</span><span class="n">n</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="mf">0.39968</span>
</span></span></code></pre>


Given that it is raining today and that it rained yesterday, write a function to calculate the probability that it will rain on the nth day after today.

Rain in N Days

<p>Let’s say that you’re working on a job recommendation engine. You have access to all user Linkedin profiles, a list of jobs each user applied to, and answers to questions that the user filled in about their job search.</p>

<p>Using this information, how would you build a job recommendation feed?</p>


<p>What problem can arise in job recommendation systems with regard to initial recommendations?</p>

What problem can arise in job recommendation systems with regard to initial recommendations?

<p>Consider a trip from one city to another that may contain many layovers. Given the list of flights out of order, each with a starting city and end city, write a function <code>plan_trip</code> to reconstruct the path of the trip so the trip tickets are in order.</p>

<p><strong>Example:</strong></p>

<p>For a trip from Bombay to Beijing:</p>

<p><strong>Input:</strong></p>

<pre tabindex="0" class="chroma"><code><span class="line"><span class="cl"><span class="n">flights</span> <span class="o">=</span> <span class="p">[</span>
</span></span><span class="line"><span class="cl">    <span class="p">[</span><span class="s1">&#39;Chennai&#39;</span><span class="p">,</span> <span class="s1">&#39;Bangalore&#39;</span><span class="p">],</span> 
</span></span><span class="line"><span class="cl">    <span class="p">[</span><span class="s1">&#39;Bombay&#39;</span><span class="p">,</span> <span class="s1">&#39;Delhi&#39;</span><span class="p">],</span> 
</span></span><span class="line"><span class="cl">    <span class="p">[</span><span class="s1">&#39;Goa&#39;</span><span class="p">,</span> <span class="s1">&#39;Chennai&#39;</span><span class="p">],</span> 
</span></span><span class="line"><span class="cl">    <span class="p">[</span><span class="s1">&#39;Delhi&#39;</span><span class="p">,</span> <span class="s1">&#39;Goa&#39;</span><span class="p">],</span> 
</span></span><span class="line"><span class="cl">    <span class="p">[</span><span class="s1">&#39;Bangalore&#39;</span><span class="p">,</span> <span class="s1">&#39;Beijing&#39;</span><span class="p">]</span>
</span></span><span class="line"><span class="cl"><span class="p">]</span>
</span></span><span class="line"><span class="cl"><span class="n">output</span> <span class="o">=</span> <span class="p">[</span>
</span></span><span class="line"><span class="cl">    <span class="p">[</span><span class="s1">&#39;Bombay&#39;</span><span class="p">,</span> <span class="s1">&#39;Delhi&#39;</span><span class="p">],</span> 
</span></span><span class="line"><span class="cl">    <span class="p">[</span><span class="s1">&#39;Delhi&#39;</span><span class="p">,</span> <span class="s1">&#39;Goa&#39;</span><span class="p">],</span> 
</span></span><span class="line"><span class="cl">    <span class="p">[</span><span class="s1">&#39;Goa&#39;</span><span class="p">,</span> <span class="s1">&#39;Chennai&#39;</span><span class="p">],</span> 
</span></span><span class="line"><span class="cl">    <span class="p">[</span><span class="s1">&#39;Chennai&#39;</span><span class="p">,</span> <span class="s1">&#39;Bangalore&#39;</span><span class="p">],</span> 
</span></span><span class="line"><span class="cl">    <span class="p">[</span><span class="s1">&#39;Bangalore&#39;</span><span class="p">,</span> <span class="s1">&#39;Beijing&#39;</span><span class="p">],</span>
</span></span><span class="line"><span class="cl"><span class="p">]</span>
</span></span></code></pre>


<p>Write a query to create a new table, named flight routes, that displays unique pairs of two locations.</p>

<p><em>Note: Duplicate pairs from the <code>flights</code> table, such as Dallas to Seattle and Seattle to Dallas, should have one entry in the flight routes table.</em></p>

<h3>Schema</h3>

<p><code>flights</code> table</p>

<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>source_location</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>destination_location</code></td>
<td>VARCHAR</td>
</tr>
</tbody>
</table>
<p><strong>Output:</strong></p>

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

<tbody>
<tr>
<td><code>destination_one</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>destination_two</code></td>
<td>VARCHAR</td>
</tr>
</tbody>
</table>

<h3>Example</h3>

<p><strong>Input:</strong></p>

<p><strong><code>flights</code> table</strong></p>

<table>
<thead>
<tr>
<th>id</th>
<th>source_location</th>
<th>destination_location</th>
</tr>
</thead>

<tbody>
<tr>
<td>1</td>
<td>Seattle, WA</td>
<td>Dallas, TX</td>
</tr>

<tr>
<td>2</td>
<td>Dallas, TX</td>
<td>Seattle, WA</td>
</tr>

<tr>
<td>3</td>
<td>Seattle, WA</td>
<td>San Francisco, CA</td>
</tr>

<tr>
<td>4</td>
<td>San Francisco, CA</td>
<td>Dallas, TX</td>
</tr>

<tr>
<td>5</td>
<td>San Francisco, CA</td>
<td>Portland, OR</td>
</tr>

<tr>
<td>6</td>
<td>Seattle, WA</td>
<td>Dallas, TX</td>
</tr>

<tr>
<td>7</td>
<td>Portland, OR</td>
<td>San Francisco, CA</td>
</tr>

<tr>
<td>8</td>
<td>San Francisco, CA</td>
<td>Los Angeles, CA</td>
</tr>
</tbody>
</table>
<p><strong>Output:</strong></p>

<table>
<thead>
<tr>
<th>destination_one</th>
<th>destination_two</th>
</tr>
</thead>

<tbody>
<tr>
<td>Dallas, TX</td>
<td>Seattle, WA</td>
</tr>

<tr>
<td>Portland, OR</td>
<td>San Francisco, CA</td>
</tr>

<tr>
<td>San Francisco, CA</td>
<td>Seattle, WA</td>
</tr>

<tr>
<td>Dallas, TX</td>
<td>San Francisco, CA</td>
</tr>

<tr>
<td>Los Angeles, CA</td>
<td>San Francisco, CA</td>
</tr>
</tbody>
</table>


<p>Given an integer <code>N</code>, write a function that returns a list of all of the prime numbers up to <code>N</code>.</p>

<p><em>Note: Return an empty list there are no prime numbers less than or equal to <code>N</code>.</em></p>

<p><strong>Example:</strong></p>

<p><strong>Input:</strong></p>

<pre tabindex="0" class="chroma"><code><span class="line"><span class="cl"><span class="n">N</span> <span class="o">=</span> <span class="mi">3</span>
</span></span></code></pre>

<p><strong>Output:</strong></p>

<pre tabindex="0" class="chroma"><code><span class="line"><span class="cl"><span class="k">def</span> <span class="nf">prime_numbers</span><span class="p">(</span><span class="n">N</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">]</span>
</span></span></code></pre>


<p>Given three tables, representing customer transactions and customer attributes:</p>

<p>Write a query to get the average order value by gender.</p>

<p><em>Note: We’re looking at the average order value by users that have ever placed an order. Additionally, please round your answer to two decimal places.</em></p>

<p><strong>Example:</strong></p>

<p><strong>Input:</strong></p>

<p><code>transactions</code> table</p>

<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>user_id</code></td>
<td>INTEGER</td>
</tr>

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

<tr>
<td><code>product_id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>quantity</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
<p><code>users</code> table</p>

<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>sex</code></td>
<td>VARCHAR</td>
</tr>
</tbody>
</table>
<p><code>products</code> table</p>

<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>price</code></td>
<td>FLOAT</td>
</tr>
</tbody>
</table>
<p><strong>Output:</strong></p>

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

<tbody>
<tr>
<td><code>sex</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>aov</code></td>
<td>FLOAT</td>
</tr>
</tbody>
</table>


Write a query to get the average order value by gender rounded to two decimal places

Average Order Value

<p>Let’s say you have a table called <code>events</code> that keeps track of every user’s actions. A small example of the <code>events</code> table would look like this:</p>

<table>
<thead>
<tr>
<th>user_id</th>
<th>created_at</th>
<th>action</th>
<th>platform</th>
</tr>
</thead>

<tbody>
<tr>
<td>1</td>
<td>2021-01-01 12:00:00</td>
<td>email_opened</td>
<td>Android</td>
</tr>

<tr>
<td>2</td>
<td>2021-01-01 12:05:00</td>
<td>like</td>
<td>Outlook</td>
</tr>

<tr>
<td>3</td>
<td>2021-01-01 12:10:00</td>
<td>post_enter</td>
<td>Gmail</td>
</tr>

<tr>
<td>4</td>
<td>2021-01-01 12:15:00</td>
<td>post_cancel</td>
<td>Outlook</td>
</tr>

<tr>
<td>5</td>
<td>2021-01-01 12:20:00</td>
<td>like</td>
<td>Gmail</td>
</tr>
</tbody>
</table>
<p>Using this information, you wish to find out how many users have opened an email.</p>

<p>What query would you use to find out?</p>

<p><strong>Example:</strong></p>

<p><strong>Input:</strong></p>

<p><code>events</code> table</p>

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

<tbody>
<tr>
<td>user_id</td>
<td>INTEGER</td>
</tr>

<tr>
<td>created_at</td>
<td>DATETIME</td>
</tr>

<tr>
<td>action</td>
<td>VARCHAR</td>
</tr>

<tr>
<td>platform</td>
<td>VARCHAR</td>
</tr>
</tbody>
</table>
<p><strong>Output:</strong></p>

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

<tbody>
<tr>
<td>num_users_open_email</td>
<td>INTEGER</td>
</tr>
</tbody>
</table>


Emails Opened

<p>You’re given two dataframes: <code>transactions</code> and <code>products</code>.</p>

<p>The transactions dataframe contains transaction ids, product ids, and the total amount of each product sold.</p>

<p>The products dataframe contains product ids and prices.</p>

<p>Write a function to return a dataframe containing every transaction with a total value of over $100. Include the total value of the transaction as a new column in the dataframe.</p>

<p><strong>Example:</strong></p>

<p><strong>Input:</strong></p>

<pre tabindex="0" class="chroma"><code><span class="line"><span class="cl"><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
</span></span><span class="line"><span class="cl">
</span></span><span class="line"><span class="cl"><span class="n">transactions</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&#34;transaction_id&#34;</span> <span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span> <span class="s2">&#34;product_id&#34;</span> <span class="p">:</span> <span class="p">[</span><span class="mi">101</span><span class="p">,</span> <span class="mi">102</span><span class="p">,</span> <span class="mi">103</span><span class="p">,</span> <span class="mi">104</span><span class="p">,</span> <span class="mi">105</span><span class="p">],</span> <span class="s2">&#34;amount&#34;</span> <span class="p">:</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">]}</span>
</span></span><span class="line"><span class="cl">
</span></span><span class="line"><span class="cl"><span class="n">products</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&#34;product_id&#34;</span> <span class="p">:</span> <span class="p">[</span><span class="mi">101</span><span class="p">,</span> <span class="mi">102</span><span class="p">,</span> <span class="mi">103</span><span class="p">,</span> <span class="mi">104</span><span class="p">,</span> <span class="mi">105</span><span class="p">],</span> <span class="s2">&#34;price&#34;</span> <span class="p">:</span> <span class="p">[</span><span class="mf">20.00</span><span class="p">,</span> <span class="mf">21.00</span><span class="p">,</span> <span class="mf">15.00</span><span class="p">,</span> <span class="mf">16.00</span><span class="p">,</span> <span class="mf">52.00</span><span class="p">]}</span>
</span></span><span class="line"><span class="cl">
</span></span><span class="line"><span class="cl"><span class="n">df_transactions</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">transactions</span><span class="p">)</span>
</span></span><span class="line"><span class="cl">
</span></span><span class="line"><span class="cl"><span class="n">df_products</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">products</span><span class="p">)</span>
</span></span></code></pre>

<p><strong>Output:</strong></p>

<table>
<thead>
<tr>
<th>transaction_id</th>
<th>product_id</th>
<th>amount</th>
<th>total_value</th>
</tr>
</thead>

<tbody>
<tr>
<td>2</td>
<td>102</td>
<td>5</td>
<td>105.00</td>
</tr>

<tr>
<td>3</td>
<td>103</td>
<td>8</td>
<td>120.00</td>
</tr>

<tr>
<td>5</td>
<td>105</td>
<td>2</td>
<td>104.00</td>
</tr>
</tbody>
</table>


<p>Let’s say you work at an advertising firm. You have a table of users’ impressions of ad campaigns over time. Each <code>impression_id</code> consists of values of user engagement specified by <code>Excited</code>, <code>OK</code>, and <code>Bored</code>.</p>

<p>Write a query to find all users who were at some point “Excited” and have never been “Bored” with a campaign.</p>

<p><strong>Example:</strong></p>

<p><strong>Input:</strong></p>

<p><code>ad_impressions</code> table:</p>

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

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

<tr>
<td><code>dt</code></td>
<td>DATETIME</td>
</tr>

<tr>
<td><code>campaign_id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>impression_id</code></td>
<td>TEXT</td>
</tr>
</tbody>
</table>
<p><strong>Output:</strong></p>

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

<tbody>
<tr>
<td><code>user_id</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>


Write a query to find all users that were at some point "Excited" and have never been "Bored" with a campaign.

Always Excited Users

<p>A jar holds 1000 coins. Out of all of the coins, 999 are fair and one is double-sided with two heads. Picking a coin at random, you toss the coin ten times.</p>

<p>Given that you see 10 heads, what is the probability that the coin is double headed and the probability that the next toss of the coin is also a head?</p>


Compute the probability the coin is double headed and the probability the next toss is a head given 10 heads

Jars and Coins

<p>How would you explain what a p-value is to someone who is not technical?</p>


<p>What does a p-value in a statistical test represent?</p>

What does a p-value in a statistical test represent?

P-value to a Layman I

<p>In a statistical test, how does a low p-value (less than 0.05) influence our decision about the null hypothesis?</p>

In a statistical test, how does a low p-value (less than 0.05) influence our decision about the null hypothesis?

P-value to a Layman II

Explain what a p-value is to someone who is not technical

P-value to a Layman

Statistics

<p>How does random forest generate the forest? Additionally, why would we use it over other algorithms such as logistic regression?</p>


<p>What happens when you average the output on multiple decision trees?</p>


What happens when you average the output on multiple decision trees?

Average Trees

How does random forest generate the forest and why use it over logistic regression

Random Forest Explanation

<p>As an accountant for a local grocery store, you have been tasked with calculating the daily sales of each product since their last restocking.</p>

<p>You have been provided with three tables: <code>products</code>, <code>sales</code>, and <code>restocking</code>. The <code>products</code> table contains information about each product, the <code>sales</code> table records the sales transactions, and the <code>restocking</code> table tracks the restocking events.</p>

<p>Write a query to retrieve the running total of sales for each product since its last restocking event.</p>

<p><strong><em>Notes:</em></strong> <em>The <code>sales_since_last_restock</code> column represents the cumulative sales of the product for each day since its last restocking event. Order the result set by <code>product_id</code>.</em></p>

<p>Example:</p>

<p><strong>Input:</strong></p>

<p>Table <code>products</code>:</p>

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

<tbody>
<tr>
<td>product_id</td>
<td>int</td>
</tr>

<tr>
<td>product_name</td>
<td>varchar</td>
</tr>
</tbody>
</table>
<p>Table <code>sales</code>:</p>

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

<tbody>
<tr>
<td>sales_id</td>
<td>int</td>
</tr>

<tr>
<td>product_id</td>
<td>int</td>
</tr>

<tr>
<td>date</td>
<td>date</td>
</tr>

<tr>
<td>sold_count</td>
<td>int</td>
</tr>
</tbody>
</table>
<p>Table <code>restocking</code>:</p>

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

<tbody>
<tr>
<td>restock_id</td>
<td>int</td>
</tr>

<tr>
<td>product_id</td>
<td>int</td>
</tr>

<tr>
<td>restock_date</td>
<td>date</td>
</tr>
</tbody>
</table>
<p><strong>Output</strong></p>

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

<tbody>
<tr>
<td>product_name</td>
<td>varchar</td>
</tr>

<tr>
<td>date</td>
<td>date</td>
</tr>

<tr>
<td>sales_since_last_restock</td>
<td>int</td>
</tr>
</tbody>
</table>


Calculate daily sales of each product since last restocking.

Cumulative Sales Since Last Restocking

<p>You’re a data analyst working for Burlington studying customer behavior during the annual Black Friday shopping spree. Each customer may visit the store multiple times throughout the day and make several purchases across different product categories. The marketing team wants to understand which customer segments were most engaged.</p>

<p>Write a SQL query to compute the following metrics for each product category:</p>

<ol>
<li>Total number of unique customers who purchased from that category</li>
<li>Average spend per customer (rounded to 2 decimal places)</li>
<li>Percentage of customers who made more than one purchase in that category (rounded to 2 decimal places)</li>
</ol>

<p>Sort the final results in descending order by average spend per customer.</p>

<p><strong><em>Notes:</em></strong></p>

<ul>
<li><em>A “purchase” means a single row in the purchases table.</em></li>
<li><em>A customer who buys multiple items in one visit still counts as multiple purchases.</em></li>
<li><em>If a customer purchased from a category only once, they should not count toward the “multiple purchase” percentage.</em></li>
<li><em>Every category present in the purchases table should appear in the output.</em></li>
</ul>

<p><strong>Schema</strong></p>

<p><strong>Input</strong></p>

<p><strong><code>purchases</code> table</strong></p>

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

<tbody>
<tr>
<td>purchase_id</td>
<td>INTEGER</td>
</tr>

<tr>
<td>customer_id</td>
<td>INTEGER</td>
</tr>

<tr>
<td>category</td>
<td>VARCHAR</td>
</tr>

<tr>
<td>amount</td>
<td>DECIMAL</td>
</tr>

<tr>
<td>purchase_time</td>
<td>DATETIME</td>
</tr>
</tbody>
</table>
<p><strong>Output</strong></p>

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

<tbody>
<tr>
<td>category</td>
<td>VARCHAR</td>
</tr>

<tr>
<td>unique_customers</td>
<td>INTEGER</td>
</tr>

<tr>
<td>avg_spend_per_customer</td>
<td>DECIMAL</td>
</tr>

<tr>
<td>pct_multi_purchase_customers</td>
<td>DECIMAL</td>
</tr>
</tbody>
</table>
<p><strong>Example</strong></p>

<p><strong>Input</strong></p>

<p><strong><code>purchases</code> table</strong></p>

<table>
<thead>
<tr>
<th>purchase_id</th>
<th>customer_id</th>
<th>category</th>
<th>amount</th>
<th>purchase_time</th>
</tr>
</thead>

<tbody>
<tr>
<td>1</td>
<td>101</td>
<td>Electronics</td>
<td>120</td>
<td>2025-11-28 09:10:00</td>
</tr>

<tr>
<td>2</td>
<td>101</td>
<td>Electronics</td>
<td>80</td>
<td>2025-11-28 10:00:00</td>
</tr>

<tr>
<td>3</td>
<td>102</td>
<td>Clothing</td>
<td>40</td>
<td>2025-11-28 11:15:00</td>
</tr>

<tr>
<td>4</td>
<td>103</td>
<td>Electronics</td>
<td>300</td>
<td>2025-11-28 13:30:00</td>
</tr>

<tr>
<td>5</td>
<td>102</td>
<td>Electronics</td>
<td>150</td>
<td>2025-11-28 14:10:00</td>
</tr>

<tr>
<td>6</td>
<td>102</td>
<td>Clothing</td>
<td>10</td>
<td>2025-11-28 16:20:00</td>
</tr>

<tr>
<td>7</td>
<td>103</td>
<td>Clothing</td>
<td>50</td>
<td>2025-11-28 17:20:00</td>
</tr>
</tbody>
</table>
<p><strong>Output</strong></p>

<table>
<thead>
<tr>
<th>category</th>
<th>unique_customers</th>
<th>avg_spend_per_customer</th>
<th>pct_multi_purchase_customers</th>
</tr>
</thead>

<tbody>
<tr>
<td>Electronics</td>
<td>3</td>
<td>216.67</td>
<td>33.33</td>
</tr>

<tr>
<td>Clothing</td>
<td>2</td>
<td>50.00</td>
<td>50.00</td>
</tr>
</tbody>
</table>
<p><strong>Explanation</strong></p>

<p>For Electronics, there are three unique customers: 101, 102, and 103. Their spend amounts are 200 for customer 101 (120 + 80), 150 for customer 102, and 300 for customer 103, giving an average spend of 216.67. Only customer 101 made more than one purchase, so the multi-purchase rate is 33.33%.</p>

<p>For Clothing, there are two unique customers: 102 and 103. Customer 102 spent a total of 50 (40 + 10), while customer 103 spent 50, resulting in an average of 50.00. Customer 102 is the only one with multiple purchases, leading to a multi-purchase rate of 50.00%.</p>


Write a SQL query to compute metrics for each product category.

Black Friday Shopping Spree

<p>Let’s say you want to test the close friends feature on Instagram Stories.</p>

<p>How would you make a control group and test group to account for network effects?</p>


<p>What could be a potential risk when Facebook segments the metrics of the user interface change by market or demographic groups?</p>

What could be a potential risk when Facebook segments the metrics of the user interface change by market or demographic groups?

Network Experiment Design I

<p>What is the primary metric that Facebook should monitor if it decides to make the user interface of its posting feature more like Instagram's?</p>

What is the primary metric that Facebook should monitor if it decides to make the user interface of its posting feature more like Instagram's?

Network Experiment Design II

<p>If Facebook changes its user interface to mimic Instagram's, which adverse effect should they anticipate and monitor?</p>

If Facebook changes its user interface to mimic Instagram's, which adverse effect should they anticipate and monitor?

Create a report displaying which shipments were delivered to customers during their membership period.

Completed Shipments

<p>Given the <code>transactions</code> table, write a query to get the max quantity purchased for each distinct <code>product_id</code>, every year. </p>

<p>The output should include the <code>year</code>, <code>product_id</code>, and <code>max_quantity</code> for that product sorted by year and <code>product_id</code> ascending.</p>

<h3>Schema:</h3>

<p><strong>Input:</strong></p>

<p><code>transactions</code> table</p>

<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>user_id</code></td>
<td>INTEGER</td>
</tr>

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

<tr>
<td><code>product_id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>quantity</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
<p><strong>Output:</strong></p>

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

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

<tr>
<td><code>product_id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>max_quantity</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>

<h3>Example</h3>

<p><strong>Input:</strong></p>

<table>
<thead>
<tr>
<th>id</th>
<th>user_id</th>
<th>created_at</th>
<th>product_id</th>
<th>quantity</th>
</tr>
</thead>

<tbody>
<tr>
<td>1</td>
<td>59</td>
<td>2019-10-05 00:00:00</td>
<td>500</td>
<td>1</td>
</tr>

<tr>
<td>2</td>
<td>84</td>
<td>2019-08-11 00:00:00</td>
<td>500</td>
<td>2</td>
</tr>

<tr>
<td>3</td>
<td>21</td>
<td>2020-01-25 00:00:00</td>
<td>500</td>
<td>3</td>
</tr>

<tr>
<td>4</td>
<td>47</td>
<td>2019-12-25 00:00:00</td>
<td>500</td>
<td>2</td>
</tr>

<tr>
<td>5</td>
<td>100</td>
<td>2020-03-05 00:00:00</td>
<td>500</td>
<td>2</td>
</tr>

<tr>
<td>6</td>
<td>45</td>
<td>2019-06-30 00:00:00</td>
<td>500</td>
<td>1</td>
</tr>

<tr>
<td>7</td>
<td>44</td>
<td>2020-07-07 00:00:00</td>
<td>500</td>
<td>1</td>
</tr>

<tr>
<td>8</td>
<td>38</td>
<td>2020-09-23 00:00:00</td>
<td>500</td>
<td>2</td>
</tr>

<tr>
<td>9</td>
<td>45</td>
<td>2020-03-08 00:00:00</td>
<td>500</td>
<td>2</td>
</tr>

<tr>
<td>10</td>
<td>16</td>
<td>2020-04-22 00:00:00</td>
<td>999</td>
<td>1</td>
</tr>

<tr>
<td>11</td>
<td>17</td>
<td>2020-06-06 00:00:00</td>
<td>999</td>
<td>2</td>
</tr>

<tr>
<td>12</td>
<td>41</td>
<td>2020-09-17 00:00:00</td>
<td>999</td>
<td>1</td>
</tr>

<tr>
<td>13</td>
<td>34</td>
<td>2019-07-05 00:00:00</td>
<td>999</td>
<td>1</td>
</tr>

<tr>
<td>14</td>
<td>78</td>
<td>2020-02-16 00:00:00</td>
<td>999</td>
<td>3</td>
</tr>

<tr>
<td>15</td>
<td>16</td>
<td>2020-05-22 00:00:00</td>
<td>999</td>
<td>4</td>
</tr>

<tr>
<td>16</td>
<td>4</td>
<td>2020-05-10 00:00:00</td>
<td>999</td>
<td>2</td>
</tr>

<tr>
<td>17</td>
<td>25</td>
<td>2019-12-15 00:00:00</td>
<td>999</td>
<td>1</td>
</tr>

<tr>
<td>18</td>
<td>71</td>
<td>2020-05-03 00:00:00</td>
<td>999</td>
<td>1</td>
</tr>

<tr>
<td>19</td>
<td>3</td>
<td>2020-08-03 00:00:00</td>
<td>999</td>
<td>2</td>
</tr>

<tr>
<td>20</td>
<td>19</td>
<td>2019-03-13 00:00:00</td>
<td>999</td>
<td>5</td>
</tr>
</tbody>
</table>
<p><strong>Output:</strong></p>

<table>
<thead>
<tr>
<th>year</th>
<th>product_id</th>
<th>max_quantity</th>
</tr>
</thead>

<tbody>
<tr>
<td>2019</td>
<td>500</td>
<td>2</td>
</tr>

<tr>
<td>2019</td>
<td>999</td>
<td>5</td>
</tr>

<tr>
<td>2020</td>
<td>500</td>
<td>3</td>
</tr>

<tr>
<td>2020</td>
<td>999</td>
<td>4</td>
</tr>
</tbody>
</table>


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