Prepare for and practice interview questions from Match group.

Match group Interview Questions

Match group Interview Guides

Data Structures & Algorithms

Given two sorted lists, write a function to merge them into one sorted list.

Merge Sorted Lists

Find Bigrams

Write a function to create a single dataframe with complete addresses in the format of street, city, state, zip code.

Complete Addresses

Write a function friendship_timeline to generate an output that lists the pairs of friends with their corresponding timestamps of the friendship beginning and then the timestamp of the friendship ending. 

Friendship Timeline

Given a list of integers, and an integer N, write a function to find all combinations that sum to the value N.

Sum to N

Write a query to select the top 3 departments with at least ten employees and rank them according to the percentage of their employees making over 100K in salary.

Employee Salaries

Select the 2nd highest salary in the engineering department

2nd Highest Salary

Write a SQL query to create a histogram of the number of comments per user in the month of January 2020.

Comments Histogram

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

Empty Neighborhoods

Subscription Overlap

Machine Learning

Encoding Categorical Features

Booking Regression

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

Rejection Reason

Job Recommendation

RMS Error

Product Sense & Metrics

How would you investigate a decline in the average number of comments per user?

Decreasing Comments

Promoting Instagram

Threaded Comments

Group Success

WAU vs Open Rates

Analytics

Write a query to create a metric that can validate and rank the queries by their search result precision.

Search Ratings

Write a query to get the distribution of the number of conversations created by each user by day in the year 2020.

Conversations Distribution

We're interested in determining if a data scientist who switches jobs more often ends up getting promoted to a manager role faster than a data scientist that stays at one job for longer. 

Career Jumping

What metrics would you use to determine the value of each marketing channel?

Marketing Channel Metrics

We're interested in how user activity affects user purchasing behavior. 

Activity Conversion

<p>Let’s say you’re working on the ads team at Facebook. Fill rate in ads is defined as the number of overall impressions divided by potential opportunities.</p>

<p>Let’s say you see that the fill rate has dipped by 10%. What would you look into?</p>


Fill Rate Drop

<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>Given a <code>employees</code> and <code>departments</code> table, select the top 3 departments with at least ten employees and rank them according to the percentage of their employees making over 100K in salary.</p>

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

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

<p><code>employees</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>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>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>
<p><strong>Output:</strong></p>

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

<tbody>
<tr>
<td><code>percentage_over_100k</code></td>
<td>FLOAT</td>
</tr>

<tr>
<td><code>department_name</code></td>
<td>VARCHAR</td>
</tr>

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


<p>Given two sorted lists, write a function to merge them into one sorted list.</p>

<p><em>Bonus: What’s the time complexity?</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">list1</span> <span class="o">=</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">5</span><span class="p">]</span>
</span></span><span class="line"><span class="cl"><span class="n">list2</span> <span class="o">=</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">6</span><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">merge_list</span><span class="p">(</span><span class="n">list1</span><span class="p">,</span><span class="n">list2</span><span class="p">)</span> <span class="o">-&gt;</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">2</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="mi">6</span><span class="p">]</span>
</span></span></code></pre>


<p>You have two sorted lists, one with m elements and one with n elements. What would be the time complexity of merging the two lists via merge sort?</p>


You have two sorted lists, one with m elements and one with n elements. What would be the time complexity of merging the two lists via merge sort? 

Merging Lists Complexity

<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>Let’s say we want to build a naive recommender. We’re given two tables, one table called <code>friends</code> with a <code>user_id</code> and <code>friend_id</code> columns representing each user’s friends, and another table called <code>page_likes</code> with a <code>user_id</code> and a <code>page_id</code> representing the page each user liked.</p>

<p>Write an SQL query to create a metric to recommend pages for each user based on recommendations from their friend’s liked pages. </p>

<p><em>Note: It shouldn’t recommend pages that the user already likes.</em></p>

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

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

<p><code>friends</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>friend_id</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
<p><code>page_likes</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>page_id</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>user_id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>page_id</code></td>
<td>INTEGER</td>
</tr>

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


Write a query to get the number of friends of a user that like a specific page

Liked Pages

<p>Given a table called <code>user_experiences</code>, write a query to determine the percentage of users that held the title of <code>&#34;Data Analyst&#34;</code> immediately before holding the title <code>&#34;Data Scientist&#34;</code>.</p>

<p>Immediate is defined as the user holding no other titles between the <code>&#34;Data Analyst&#34;</code> and <code>&#34;Data Scientist&#34;</code> roles.</p>

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

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

<p><code>user_experiences</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>position_name</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>start_date</code></td>
<td>DATETIME</td>
</tr>

<tr>
<td><code>end_date</code></td>
<td>DATETIME</td>
</tr>

<tr>
<td><code>user_id</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>percentage</code></td>
<td>FLOAT</td>
</tr>
</tbody>
</table>


User Experience Percentage

<p>Write a SQL query to create a histogram of the number of comments per user in the month of January 2020.</p>

<p><em>Note: Assume bin buckets class intervals of one.</em></p>

<p><em>Note: Comments that were created outside of January 2020 should be counted in a “0” bucket</em></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>created_at</code></td>
<td>DATETIME</td>
</tr>

<tr>
<td><code>neighborhood_id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>mail</code></td>
<td>VARCHAR</td>
</tr>
</tbody>
</table>
<p><code>comments</code> table</p>

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

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

<tr>
<td><code>body</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>created_at</code></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><code>comment_count</code></td>
<td>INTEGER</td>
</tr>

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


<p>Given the <code>employees</code> and <code>departments</code> table, write a query to get the top 3 highest employee salaries by department. If the department contains less that 3 employees, the top 2 or the top 1 highest salaries should be listed (assume that each department has at least 1 employee). </p>

<p><em>Note: The output should include the full name of the employee in one column, the department name, and the salary. The output should be sorted by department name in ascending order and salary in descending order.</em> </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>employee_nam</code>e</td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>department_name</code></td>
<td>VARCHAR</td>
</tr>

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


Get the top 3 highest employee salaries by department

Top Three Salaries

<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>


Last Transaction

<p>Let’s say that your company is running a standard control and variant AB test on a feature to increase conversion rates on the landing page. The PM checks the results and finds a .04 p-value.</p>

<p>How would you assess the validity of the result?</p>


Experiment Validity

A/B Testing

<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?

Network Experiment Design III

Network Experiment Design

<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

Button AB Test

<p>Given a table of product subscriptions with a subscription start date and end date for each user, write a query that returns true or false whether or not each user has a subscription date range that overlaps with any other completed subscription.</p>

<p>Completed subscriptions have <code>end_date</code> recorded.</p>

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

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

<p><code>subscriptions</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>start_date</code></td>
<td>DATETIME</td>
</tr>

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

<table>
<thead>
<tr>
<th><code>user_id</code></th>
<th><code>start_date</code></th>
<th><code>end_date</code></th>
</tr>
</thead>

<tbody>
<tr>
<td>1</td>
<td>2019-01-01</td>
<td>2019-01-31</td>
</tr>

<tr>
<td>2</td>
<td>2019-01-15</td>
<td>2019-01-17</td>
</tr>

<tr>
<td>3</td>
<td>2019-01-29</td>
<td>2019-02-04</td>
</tr>

<tr>
<td>4</td>
<td>2019-02-05</td>
<td>2019-02-10</td>
</tr>
</tbody>
</table>
<p><strong>Output:</strong></p>

<table>
<thead>
<tr>
<th><code>user_id</code></th>
<th><code>overlap</code></th>
</tr>
</thead>

<tbody>
<tr>
<td>1</td>
<td>1</td>
</tr>

<tr>
<td>2</td>
<td>1</td>
</tr>

<tr>
<td>3</td>
<td>1</td>
</tr>

<tr>
<td>4</td>
<td>0</td>
</tr>
</tbody>
</table>


<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

P-value to a Layman

Statistics

<p>Given the tables <code>users</code> and <code>rides</code>, write a query to report the distance traveled by each user in descending order.</p>

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

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

<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>INTEGER</td>
</tr>
</tbody>
</table>
<p><code>rides</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>passenger_user_id</code></td>
<td>INTEGER</td>
</tr>

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

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


Distance Traveled

<p>Given the <code>transactions</code> table below, write a query that finds the third purchase of every user.</p>

<p><em>Note: Sort the results by the <code>user_id</code> in ascending order. If a user purchases two products at the same time, the lower <code>id</code> field is used to determine which is the first purchase.</em></p>

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

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

<p><code>transactions</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>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>Columns</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<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>


Third Purchase

<p>Given a table of user sessions, write a query to get the number of days between each user’s first session and last session for the year 2020. </p>

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

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

<p><code>user_sessions</code> table</p>

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

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

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

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

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


Find the difference between users' first and last sessions in the year

Session Difference

<p>You’re given a dataframe of students named <code>students_df</code>:</p>

<p><code>students_df</code> table</p>

<table>
<thead>
<tr>
<th><strong>name</strong></th>
<th><strong>age</strong></th>
<th><strong>favorite_color</strong></th>
<th><strong>grade</strong></th>
</tr>
</thead>

<tbody>
<tr>
<td>Tim Voss</td>
<td>19</td>
<td>red</td>
<td>91</td>
</tr>

<tr>
<td>Nicole Johnson</td>
<td>20</td>
<td>yellow</td>
<td>95</td>
</tr>

<tr>
<td>Elsa Williams</td>
<td>21</td>
<td>green</td>
<td>82</td>
</tr>

<tr>
<td>John James</td>
<td>20</td>
<td>blue</td>
<td>75</td>
</tr>

<tr>
<td>Catherine Jones</td>
<td>23</td>
<td>green</td>
<td>93</td>
</tr>
</tbody>
</table>
<p>Write a function named <code>grades_colors</code> to select only the rows where the student’s favorite color is green or red and their grade is above 90.</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">students</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&#34;name&#34;</span> <span class="p">:</span> <span class="p">[</span><span class="s2">&#34;Tim Voss&#34;</span><span class="p">,</span> <span class="s2">&#34;Nicole Johnson&#34;</span><span class="p">,</span> <span class="s2">&#34;Elsa Williams&#34;</span><span class="p">,</span> <span class="s2">&#34;John James&#34;</span><span class="p">,</span> <span class="s2">&#34;Catherine Jones&#34;</span><span class="p">],</span> <span class="s2">&#34;age&#34;</span> <span class="p">:</span> <span class="p">[</span><span class="mi">19</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">21</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">23</span><span class="p">],</span> <span class="s2">&#34;favorite_color&#34;</span> <span class="p">:</span> <span class="p">[</span><span class="s2">&#34;red&#34;</span><span class="p">,</span> <span class="s2">&#34;yellow&#34;</span><span class="p">,</span> <span class="s2">&#34;green&#34;</span><span class="p">,</span> <span class="s2">&#34;blue&#34;</span><span class="p">,</span> <span class="s2">&#34;green&#34;</span><span class="p">],</span> <span class="s2">&#34;grade&#34;</span> <span class="p">:</span> <span class="p">[</span><span class="mi">91</span><span class="p">,</span> <span class="mi">95</span><span class="p">,</span> <span class="mi">82</span><span class="p">,</span> <span class="mi">75</span><span class="p">,</span> <span class="mi">93</span><span class="p">]}</span>
</span></span><span class="line"><span class="cl">
</span></span><span class="line"><span class="cl"><span class="n">students_df</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">students</span><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">grades_colors</span><span class="p">(</span><span class="n">students_df</span><span class="p">)</span> <span class="o">-&gt;</span>
</span></span></code></pre>

<table>
<thead>
<tr>
<th><strong>name</strong></th>
<th><strong>age</strong></th>
<th><strong>favorite_color</strong></th>
<th><strong>grade</strong></th>
</tr>
</thead>

<tbody>
<tr>
<td>Tim Voss</td>
<td>19</td>
<td>red</td>
<td>91</td>
</tr>

<tr>
<td>Catherine Jones</td>
<td>23</td>
<td>green</td>
<td>93</td>
</tr>
</tbody>
</table>


Write a function to select only the rows where the student's favorite color is green or red and their grade is above 90.

Good Grades and Favorite Colors

<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>Imagine a deck of 500 cards numbered from 1 to 500. If all the cards are shuffled randomly and you are asked to pick three cards, one at a time, what’s the probability of each subsequent card being larger than the previous drawn card?</p>


<p>Imagine a deck of 2000 cards numbered from 1 to 2000. If all the cards are shuffled randomly and you are asked to pick three cards, one at a time, what’s the probability of each subsequent card being larger than the previously drawn card?</p>


Imagine a deck of 2000 cards numbered from 1 to 2000. If all the cards are shuffled randomly and you are asked to pick three cards, one at a time, what's the probability of each subsequent card being larger than the previously drawn card?

2000 cards

500 Cards

Probability

<p>You’re given three tables.</p>

<p>An <strong>ads</strong> table holds an ID and the advertisement name like “Labor day shirts sale”. The <strong>feed_comments</strong> table holds the comments on ads by different users that occur in the regular feed. The <strong>moments_comments</strong> table holds the comments on ads by different users in the moments section.</p>

<p>Write a query to get the percentage of comments, by ad, that occurs in the feed versus mentions sections of the app.</p>

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

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

<p><code>feed_comments</code> table</p>

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

<tbody>
<tr>
<td><code>ad_id</code></td>
<td>integer</td>
</tr>

<tr>
<td><code>user_id</code></td>
<td>integer</td>
</tr>

<tr>
<td><code>comment_id</code></td>
<td>integer</td>
</tr>
</tbody>
</table>
<p><code>moments_comments</code> table</p>

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

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

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

<tr>
<td><code>comment_id</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
<p><code>ads</code> table</p>

<table>
<thead>
<tr>
<th><strong>column</strong></th>
<th><strong>type</strong></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>name</th>
<th>percentage_feed</th>
<th>percentage_moments</th>
</tr>
</thead>

<tbody>
<tr>
<td>Labor Day</td>
<td>.6</td>
<td>.4</td>
</tr>

<tr>
<td>Polo Shirts</td>
<td>.85</td>
<td>.15</td>
</tr>
</tbody>
</table>


Write a query to get the percentage of comments, by ad, that occurs in the feed versus mentions sections of the app.

Ad Comments

<p>The <code>events</code> table tracks every time a user performs a certain action (like, post_enter, etc.) on a platform. An example of this 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>What query would you write to determine how many different users gave a like on June 6, 2020?</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_gave_like</td>
<td>INTEGER</td>
</tr>
</tbody>
</table>


Find out the number of users that gave a like on a certain date

Like Tracker

<p>Given two tables:  <code>accounts</code>, and <code>downloads</code>, find the average number of downloads for free vs paying accounts, broken down by day.</p>

<p><em>Note: You only need to consider accounts that have had at least one download before when calculating the average.</em></p>

<p><em>Note: round <code>average_downloads</code> to 2 decimal places.</em></p>

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

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

<p><code>accounts</code> table</p>

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

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

<tr>
<td><code>paying_customer</code></td>
<td>BOOLEAN</td>
</tr>
</tbody>
</table>
<p><code>downloads</code> table</p>

<table>
<thead>
<tr>
<th>column</th>
<th>type</th>
</tr>
</thead>

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

<tr>
<td><code>download_date</code></td>
<td>DATETIME</td>
</tr>

<tr>
<td><code>downloads</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>download_date</code></td>
<td>DATETIME</td>
</tr>

<tr>
<td><code>paying_customer</code></td>
<td>BOOLEAN</td>
</tr>

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


Download Facts

<p>Let’s say you are designing a marketplace for your website.</p>

<p>Selling firearms is prohibited by your website’s Terms of Service Agreement (not to mention the laws of your country). To this end, you want to create a system that can automatically detect if a listing on the marketplace is selling a gun.</p>

<p>How would you go about doing this?</p>


<p>While designing a system to detect firearms listings on a marketplace website, what metrics could be used to minimize both false positives and false negatives?</p>

While designing a system to detect firearms listings on a marketplace website, what metrics could be used to minimize both false positives and false negatives?

Detecting Firearm Sales

How would you build a model to detect if a post on a marketplace is talking about selling a gun?

<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>


Flight Records

<p>Given a dictionary with weights, write a function <code>random_key</code> that returns a key at random with a probability proportional to the weights.</p>

<p><strong>Example 1:</strong></p>

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

<pre tabindex="0" class="chroma"><code><span class="line"><span class="cl"><span class="n">weights</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;A&#39;</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span> <span class="s1">&#39;B&#39;</span><span class="p">:</span> <span class="mi">2</span><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="n">random_key</span><span class="p">(</span><span class="n">weights</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="k">return</span> <span class="n">A</span> <span class="mi">1</span><span class="o">/</span><span class="mi">3</span> <span class="n">of</span> <span class="n">the</span> <span class="n">time</span><span class="p">,</span> <span class="n">B</span> <span class="mi">2</span><span class="o">/</span><span class="mi">3</span> <span class="n">of</span> <span class="n">the</span> <span class="n">time</span>
</span></span></code></pre>

<p><strong>Example 2:</strong></p>

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

<pre tabindex="0" class="chroma"><code><span class="line"><span class="cl"><span class="n">weights</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;A&#39;</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span> <span class="s1">&#39;B&#39;</span><span class="p">:</span> <span class="mi">1</span><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="n">random_key</span><span class="p">(</span><span class="n">weights</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="k">return</span> <span class="n">A</span> <span class="mi">1</span><span class="o">/</span><span class="mi">2</span>  <span class="n">of</span> <span class="n">the</span> <span class="n">time</span><span class="p">,</span> <span class="n">B</span> <span class="mi">1</span><span class="o">/</span><span class="mi">2</span>  <span class="n">of</span> <span class="n">the</span> <span class="n">time</span>
</span></span></code></pre>


Weighted Keys

<p>Given a table called <code>employees</code>, get the largest salary of any employee by department</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>department</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>salary</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>department</code></td>
<td>VARCHAR</td>
</tr>

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


Write a query to get the largest salary of any employee by department

Largest Salary by Department

<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 are a data scientist at YouTube focused on creators.</p>

<p>A PM comes to you worried that amateur video creators could do well before but now it seems like only “superstars” do well.</p>

<p>What data points and metrics would you look at to decide if this is true or not?</p>


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