Prepare for and practice interview questions from ADM across topics like SQL, Machine Learning, Statistics and more.

ADM Interview Questions

ADM Interview Guides

A/B Testing

How would you assess the validity of the result?

Experiment Validity

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

Delivery Estimate Model

Button AB Test

A/B Test Power Size

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

Network Experiment Design

Product Sense & Metrics

How can we measure the success of a 30-day free trial in acquiring and retaining new Netflix users

Netflix Retention

How would you measure the success of Facebook Groups

Group Success

What would you investigate to diagnose what's happening?

WAU vs Open Rates

How would you measure the success of the Instagram TV product

Instagram TV Success

How would you investigate the drop in Facebook composer posts per user, especially photo posts

Post Composer Drop

Machine Learning

How would you encode a categorical variable with thousands of distinct values

Encoding Categorical Features

Write a function calculate_rmse to calculate the root mean squared error from y_pred and y_true

RMS Error

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

Bagging vs Boosting

What are the logistic and softmax functions? What is the difference between the two?

Softmax vs Logistic

What are some reasons why measuring bias would be important in building this particular model

Food Delivery Times

Select the 2nd highest salary in the engineering department

2nd Highest Salary

Longest Streak Users

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

Average Order Value

Write a query to find the average downloads for free vs paying accounts by day, rounded to 2 decimals

Download Facts

Write a SQL query to count transactions filtered by several criterias.

Count Transactions

Data Structures & Algorithms

Given two nonempty lists of user_ids and tips, write a function to find the user that tipped the most.

Biggest Tip

Write a function moving_window to find the moving window average

Moving Window

Given a json string with nested objects, write a function that flattens all the objects to a single key-value dictionary.

Flatten JSON

Write a function to find how many friends each person has.

How Many Friends

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

<p>Let’s say our company currently sources 80% of its electronics components from factories in China, but a new 50% tariff on Chinese imports is about to take effect.</p>

<p>How would you approach re-designing our supply chain to minimize costs, and what steps would you take to estimate the resulting financial impact?</p>


How would you redesign the supply chain and estimate financial impact after a major China tariff?

China Tariff Supply Chain

Supply Chain

<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 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>


<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>


<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

<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>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>


<p>Over budget on a project is defined when the salaries, prorated to the day, exceed the budget of the project.</p>

<p>For example, if Alice and Bob both combined income make 200K and work on a project of a budget of 50K that takes half a year, then the project is over budget given 0.5 * 200K = 100K &gt; 50K.</p>

<p>Write a query to forecast the budget for all projects and return a label of <code>&#34;overbudget&#34;</code> if it is over budget and <code>&#34;within budget&#34;</code> otherwise.</p>

<p><em>Note: Assume that employees only work on one project at a time.</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>projects</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>title</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>state_date</code></td>
<td>DATETIME</td>
</tr>

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

<tr>
<td><code>budget</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
<p><strong>departments</strong> 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><code>employee_projects</code> table</p>

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

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

<tr>
<td><code>employee_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>title</code></td>
<td>VARCHAR</td>
</tr>

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


Write a query to forecast each project's budget and label it overbudget or within budget

Over-Budget Projects

<p>Let’s say you work at Uber. You’re getting reports that riders are complaining about the Uber map showing wrong location pickup spots.</p>

<p>How would you go about verifying how frequently this is happening?</p>

<p><em>Note: Assume you have user location data but not external data sources.</em></p>


You're getting reports that riders are complaining about the Uber map showing wrong location pickup spots. How would you go about verifying how frequently this is happening?

Uber User Journey

Analytics

<p>Given a table with event logs, find the top five users with the longest continuous streak of visiting the platform.</p>

<p><em>Note: A continuous streak counts if the user visits the platform at least once per day on consecutive days.</em></p>

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

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

<p><code>events</code> table</p>

<table>
<thead>
<tr>
<th><code>Column</code></th>
<th><code>Type</code></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>url</code></td>
<td>VARCHAR</td>
</tr>
</tbody>
</table>
<p><strong>Output:</strong></p>

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

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

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


<p>Tell me about a data project that didn’t go the way you expected. What did you set out to do, what surprised you, and how did you handle it?</p>


Describing a data project and its challenges

Hurdles In Data Projects

Behavioral

<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>Given two strings, write a function to return <code>True</code> if the strings are anagrams of each other and <code>False</code> if they are not.</p>

<p><em>Note: A word is not an anagram of itself.</em></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">string_1</span> <span class="o">=</span> <span class="s2">&#34;listen&#34;</span>
</span></span><span class="line"><span class="cl"><span class="n">string_2</span> <span class="o">=</span> <span class="s2">&#34;silent&#34;</span>
</span></span></code></pre>

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

<pre tabindex="0" class="chroma"><code><span class="line"><span class="cl"><span class="kc">True</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">string_1</span> <span class="o">=</span> <span class="s2">&#34;banana&#34;</span>
</span></span><span class="line"><span class="cl"><span class="n">string_2</span> <span class="o">=</span> <span class="s2">&#34;bandana&#34;</span>
</span></span></code></pre>

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

<pre tabindex="0" class="chroma"><code><span class="line"><span class="cl"><span class="kc">False</span>
</span></span></code></pre>


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

Valid Anagram

<p>Let’s say you have a categorical variable with thousands of distinct values, how would you encode it?</p>


<p>Which method can be used to extract communities from large networks, that does not require a pre-determined number of clusters like K-means?</p>

Which method can be used to extract communities from large networks, that does not require a pre-determined number of clusters like K-means?

<p>Let’s say at Netflix we offer a subscription where customers can enroll for a 30-day free trial. After 30 days, customers will be automatically charged based on the package selected.</p>

<p>Let’s say we want to measure the success of acquiring new users through the free trial.</p>

<p>How can we measure acquisition success and what metrics can we use to measure the success of the free trial?</p>


<p>Let’s say we want to measure the effectiveness of Netflix’s 30-day free trial plan.</p>

<p>What is NOT a metric we would use?</p>


Let's say we want to measure the effectiveness of Netflix's 30-day free trial plan.

What is NOT a metric we would use?

<p>Let’s say you work at Costco. Costco has a database with two tables. The first is <code>users</code> composed of user information, including their registration date, and the second is <code>purchases</code> that has the entire item purchase history (if any) for those users.</p>

<p>Write a query to get the total amount spent on each item in the ‘purchases’ table by users that registered in 2022.</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>user_id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>user_name</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>registration_date</code></td>
<td>DATETIME</td>
</tr>
</tbody>
</table>
<p><code>purchases</code> table</p>

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

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

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

<tr>
<td><code>item</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>price</code></td>
<td>FLOAT</td>
</tr>

<tr>
<td><code>purchase_date</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>item</code></td>
<td>VARCHAR</td>
</tr>

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


Total Spent on Products

<p>Let’s say that you’re a data scientist on the engagement team. A product manager comes up to you and says that the weekly active users metric is up 5% but email notification open rates are down 2%.</p>

<p>What would you investigate to diagnose what’s happening?</p>


Email Open Rate

<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

<p>Let’s say we want to build a new delivery time estimate model for consumers ordering food delivery.</p>

<p>How would you determine if the new model predicts delivery times better than the old model?</p>


<p>When rolling out a new delivery time estimate model in production, what is an important consideration?</p>

When rolling out a new delivery time estimate model in production, what is an important consideration?

Delivery Estimate Model I

<p>What is one way to measure the performance of a new delivery time estimate model for food delivery?</p>

What is one way to measure the performance of a new delivery time estimate model for food delivery?

Delivery Estimate Model II

<p>In an A/B test, how can you check if assignment to the various buckets was truly random?</p>


<p>In an A/B test, which of the following is a common method to determine if the assignment to the test groups was truly random?</p>

In an A/B test, which of the following is a common method to determine if the assignment to the test groups was truly random?

Random Bucketing

How can you check if assignment to the various buckets was truly random

<p>Write a function <code>calculate_rmse</code> to calculate the root mean squared error of a regression model. The function should take in two lists, one that represents the predictions <code>y_pred</code> and another with the target values <code>y_true</code>.</p>

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

<p><strong>Example 1: </strong></p>

<pre tabindex="0" class="chroma"><code><span class="line"><span class="cl"><span class="n">y_pred</span> <span class="o">=</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></span><span class="line"><span class="cl"><span class="n">y_true</span> <span class="o">=</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></span><span class="line"><span class="cl">
</span></span><span class="line"><span class="cl"><span class="k">def</span> <span class="nf">calculate_rmse</span><span class="p">(</span><span class="n">y_pred</span><span class="p">,</span><span class="n">y_true</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="mi">0</span>
</span></span></code></pre>

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

<pre tabindex="0" class="chroma"><code><span class="line"><span class="cl"><span class="n">y_pred</span> <span class="o">=</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></span><span class="line"><span class="cl"><span class="n">y_true</span> <span class="o">=</span> <span class="p">[</span><span class="mi">5</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">3</span><span class="p">]</span>
</span></span><span class="line"><span class="cl">
</span></span><span class="line"><span class="cl"><span class="k">def</span> <span class="nf">calculate_rmse</span><span class="p">(</span><span class="n">y_pred</span><span class="p">,</span><span class="n">y_true</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="mf">1.63</span>
</span></span></code></pre>


<p>Let’s say you’re a Product Data Scientist at Instagram. How would you measure the success of the Instagram TV product?</p>


<p>What is the primary goal of Instagram TV as a product feature?</p>

What is the primary goal of Instagram TV as a product feature?

<p>How would you measure the success of Facebook Groups?</p>


<p>Considering the purpose of Facebook Groups as a platform for connecting users through shared interests or real-life relationships, which metrics would be appropriate to measure the success of these groups?</p>


Considering the purpose of Facebook Groups as a platform for connecting users through shared interests or real-life relationships, which metrics would be appropriate to measure the success of these groups?

<p>Given a table of transactions and a table of users, write a query to determine if users tend to order more to their primary address versus other addresses.</p>

<p><em>Note: Return the percentage of transactions ordered to their home address as <code>home_address_percent</code>.</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>shipping_address</code></td>
<td>VARCHAR</td>
</tr>
</tbody>
</table>
<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>address</code></td>
<td>VARCHAR</td>
</tr>
</tbody>
</table>
<p><strong>Example Output:</strong></p>

<table>
<thead>
<tr>
<th><code>home_address_percent</code></th>
<th>0.76</th>
</tr>
</thead>

<tbody>
</tbody>
</table>


Order Addresses

<p>Let’s say that on Facebook composer, the posting tool, drops from 3% posts per user last month to 2.5% post per user today. </p>

<p>How would you investigate what happened?</p>

<p>Let’s say the drop is in photo posts. What would you investigate now?</p>


<p>In the analysis of a drop in photo posts per user on a social media site like Facebook, which factor(s) could influence the change in the average number of posts per user?</p>


In the analysis of a drop in photo posts per user on a social media site like Facebook, which factor(s) could influence the change in the average number of posts per user?

<p>Lime’s consumer app team wants to measure the number of users who return to the app after their initial login. Given a user_logins table that records each user’s login by date, write a query to produce a daily summary with retention and engagement metrics.</p>

<p>For each date that appears in the user_logins table, calculate daily retention metrics including cohort size, 7-day retention, retention rate, and rolling 7-day active users. <em>(A cohort for a given date consists of all users who logged in on that specific date (regardless of whether it was their first login or a subsequent login)</em>.</p>

<p>Notes:</p>

<ul>
<li>A user is considered retained if they log in again within the next 7 days after their cohort date (days 2-8), excluding the cohort day itself.</li>
<li>The rolling 7-day users includes all distinct users who logged in during the 7-day window ending on the current date (from login_date - 6 days to login_date, inclusive).</li>
<li>Each (user_id, login_date) pair is unique - users can only log in once per day.
The output should be ordered by login_date ascending.</li>
<li>Retention rate should be calculated as: (retained_within_7d / cohort_users) * 100, rounded to 1 decimal place.</li>
</ul>

<h3><strong>Schema:</strong></h3>

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

<p><code>user_logins</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>login_date</code></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><code>login_date</code></td>
<td>DATE</td>
</tr>

<tr>
<td><code>cohort_users</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>retained_within_7d</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>retention_rate</code></td>
<td>DECIMAL(3, 2)</td>
</tr>

<tr>
<td><code>rolling_7d_users</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>

<h3><strong>Example:</strong></h3>

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

<table>
<thead>
<tr>
<th>user_id</th>
<th>login_date</th>
</tr>
</thead>

<tbody>
<tr>
<td>1</td>
<td>2024-06-01</td>
</tr>

<tr>
<td>2</td>
<td>2024-06-01</td>
</tr>

<tr>
<td>1</td>
<td>2024-06-02</td>
</tr>

<tr>
<td>2</td>
<td>2024-06-08</td>
</tr>

<tr>
<td>3</td>
<td>2024-06-08</td>
</tr>

<tr>
<td>1</td>
<td>2024-06-09</td>
</tr>

<tr>
<td>3</td>
<td>2024-06-10</td>
</tr>
</tbody>
</table>
<p><strong>Output:</strong></p>

<table>
<thead>
<tr>
<th>login_date</th>
<th>cohort_users</th>
<th>retained_within_7d</th>
<th>retention_rate</th>
<th>rolling_7d_users</th>
</tr>
</thead>

<tbody>
<tr>
<td>2024-06-01</td>
<td>2</td>
<td>1</td>
<td>50.0</td>
<td>2</td>
</tr>

<tr>
<td>2024-06-02</td>
<td>1</td>
<td>1</td>
<td>100.0</td>
<td>2</td>
</tr>

<tr>
<td>2024-06-08</td>
<td>2</td>
<td>1</td>
<td>50.0</td>
<td>2</td>
</tr>

<tr>
<td>2024-06-09</td>
<td>1</td>
<td>0</td>
<td>0.0</td>
<td>2</td>
</tr>

<tr>
<td>2024-06-10</td>
<td>1</td>
<td>0</td>
<td>0.0</td>
<td>2</td>
</tr>
</tbody>
</table>
<p><strong>Explanation</strong>:</p>

<p>Retention is calculated for users who return within 7 days after their first login on a given day. Rolling 7-day user counts unique users over the current and previous 6 days.*</p>

<p>Suppose the current <strong><code>login_date</code></strong> is <strong>October 10, 2027</strong>.</p>

<ul>
<li>The <strong>cohort</strong> for this date includes all users who logged in on <em>2027-10-10</em>.</li>
<li>A user is considered <strong>retained</strong> if they log in <strong>again between 2027-10-11 and 2027-10-17</strong> (inclusive).</li>
<li>The cohort day itself (<strong>2027-10-10</strong>) is <strong>excluded</strong> from this retention window.</li>
</ul>


How would you calculate daily user retention and rolling activity for Lime's app using login data?

Daily Retention Summary

<p>You’re a data scientist working for a smart grocery platform that helps families automatically plan their Christmas dinner based on their dietary preferences and pantry availability.</p>

<p>Each Christmas recipe consists of several ingredients, and each ingredient has different brands with different prices. Families want the cheapest possible ingredient combination but only using brands they have marked as acceptable and only those ingredients they do not already have in their pantry.</p>

<p>Write a SQL query to determine the cheapest valid shopping cost for each recipe for family_id = 1, while also identifying whether the recipe is feasible to make.</p>

<p>For each recipe, compute:</p>

<ol>
<li><strong><code>missing_ingredients</code>:</strong> number of ingredients the family does not already have in their pantry</li>
<li><strong><code>min_total_cost</code>:</strong> total minimum cost required to buy missing ingredients</li>
<li>feasible:

<ul>
<li><code>&#39;Yes&#39;</code> if every missing ingredient has at least one acceptable brand available</li>
<li><code>&#39;No&#39;</code> otherwise (and <code>min_total_cost</code> should be <code>NULL</code>)</li>
</ul></li>
</ol>

<p><strong><em>Note:</em></strong> <em>Sort results by <code>missing_ingredients</code> ASC and <code>min_total_cost</code> ASC (NULLS LAST)</em></p>

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

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

<p><strong><code>recipes</code> Table</strong></p>

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

<tbody>
<tr>
<td>recipe_id</td>
<td>INTEGER</td>
</tr>

<tr>
<td>ingredient_id</td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
<p><strong><code>ingredient_brands</code> Table</strong></p>

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

<tbody>
<tr>
<td>ingredient_id</td>
<td>INTEGER</td>
</tr>

<tr>
<td>brand_id</td>
<td>INTEGER</td>
</tr>

<tr>
<td>price</td>
<td>DECIMAL</td>
</tr>
</tbody>
</table>
<p><strong><code>pantry</code> Table</strong></p>

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

<tbody>
<tr>
<td>family_id</td>
<td>INTEGER</td>
</tr>

<tr>
<td>ingredient_id</td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
<p><strong><code>acceptable_brands</code> Table</strong></p>

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

<tbody>
<tr>
<td>family_id</td>
<td>INTEGER</td>
</tr>

<tr>
<td>brand_id</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>recipe_id</td>
<td>INTEGER</td>
</tr>

<tr>
<td>missing_ingredients</td>
<td>INTEGER</td>
</tr>

<tr>
<td>min_total_cost</td>
<td>DECIMAL</td>
</tr>

<tr>
<td>feasible</td>
<td>VARCHAR</td>
</tr>
</tbody>
</table>
<p><strong>Example</strong></p>

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

<p><strong><code>recipes</code> Table</strong></p>

<table>
<thead>
<tr>
<th>recipe_id</th>
<th>ingredient_id</th>
</tr>
</thead>

<tbody>
<tr>
<td>1</td>
<td>100</td>
</tr>

<tr>
<td>1</td>
<td>200</td>
</tr>

<tr>
<td>1</td>
<td>300</td>
</tr>

<tr>
<td>2</td>
<td>200</td>
</tr>

<tr>
<td>2</td>
<td>400</td>
</tr>
</tbody>
</table>
<p><strong><code>pantry</code> Table</strong></p>

<table>
<thead>
<tr>
<th>family_id</th>
<th>ingredient_id</th>
</tr>
</thead>

<tbody>
<tr>
<td>1</td>
<td>100</td>
</tr>
</tbody>
</table>
<p><strong><code>ingredient_brands</code> Table</strong></p>

<table>
<thead>
<tr>
<th>ingredient_id</th>
<th>brand_id</th>
<th>price</th>
</tr>
</thead>

<tbody>
<tr>
<td>200</td>
<td>10</td>
<td>3.50</td>
</tr>

<tr>
<td>200</td>
<td>11</td>
<td>2.99</td>
</tr>

<tr>
<td>300</td>
<td>20</td>
<td>8.50</td>
</tr>

<tr>
<td>400</td>
<td>30</td>
<td>6.75</td>
</tr>
</tbody>
</table>
<p><strong><code>acceptable_brands</code> Table</strong></p>

<table>
<thead>
<tr>
<th>family_id</th>
<th>brand_id</th>
</tr>
</thead>

<tbody>
<tr>
<td>1</td>
<td>10</td>
</tr>

<tr>
<td>1</td>
<td>20</td>
</tr>

<tr>
<td>1</td>
<td>30</td>
</tr>
</tbody>
</table>
<p><strong>Output</strong></p>

<table>
<thead>
<tr>
<th>recipe_id</th>
<th>missing_ingredients</th>
<th>min_total_cost</th>
<th>feasible</th>
</tr>
</thead>

<tbody>
<tr>
<td>1</td>
<td>2</td>
<td>12.00</td>
<td>Yes</td>
</tr>

<tr>
<td>2</td>
<td>2</td>
<td>10.25</td>
<td>Yes</td>
</tr>
</tbody>
</table>

<h3><strong>Explanation</strong></h3>

<p>For Recipe 1, the required ingredients are 100, 200, and 300. The pantry already contains ingredient 100, so only 200 and 300 are missing. The cheapest acceptable option for ingredient 200 is brand 10 at $3.50, since brand 11 is not allowed. For ingredient 300, the cheapest acceptable option is brand 20 at $8.50. This makes the total cost $12.00, and the recipe is feasible.</p>

<p>For Recipe 2, the required ingredients are 200 and 400. The cheapest acceptable choice for ingredient 200 is again $3.50, and for ingredient 400 it is $6.75. The total comes to $10.25, and this recipe is also feasible.</p>


Determine the cheapest valid shopping cost for each recipe for family_id = 1 and indicate whether each recipe is feasible

Christmas Dinner Ingredient Optimization

<p>Let’s say that you’re the PM on Google Maps.</p>

<ol>
<li><p>How would you improve Google Maps?</p></li>

<li><p>What metrics would you check to see if your feature improvements are successful?</p></li>
</ol>


Google Maps Improvement

<p>Let’s say we have an <code>annual_payments</code> table. Answer the following three questions via SQL queries and output them as a table with the answers to each question.</p>

<ol>
<li>How many total transactions are in this table?</li>
<li>How many different users made transactions?</li>
<li>How many transactions listed as <code>&#34;paid&#34;</code> have an amount greater or equal to 100?</li>
<li>Which product made the highest revenue? (use only transactions with a <code>&#34;paid&#34;</code> status)</li>
</ol>

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

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

<p><code>annual_payments</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>amount</code></td>
<td>FLOAT</td>
</tr>

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

<tr>
<td><code>status</code></td>
<td>VARCHAR</td>
</tr>

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

<tr>
<td><code>amount_refunded</code></td>
<td>FLOAT</td>
</tr>

<tr>
<td><code>product</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>last_updated</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>question_id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>answer</code></td>
<td>TEXT</td>
</tr>
</tbody>
</table>


<p>Let’s say you’re interning at a fintech startup that’s building a platform for online games of chance. During testing, one of your simulations flips a coin 10 times and gets 8 tails and 2 heads.</p>

<p>How would you determine if the coin is fair, and what statistical approach would you use to support your conclusion?</p>


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

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