Prepare for and practice interview questions from Agero across topics like Statistics, Analytics, Python and more.

Agero Interview Questions

Agero Interview Guides

Machine Learning

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

Encoding Categorical Features

How would we build a model to detect fraud and text customers to approve or deny fraudulent transactions

Bank Fraud Model

How would we know if we have enough data to create an accurate enough model?

One Million Rides

Lasso vs Ridge

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

Bagging vs Boosting

Probability

First to Six

What is the probability that each subsequent card is larger than the previous drawn card when picking three cards from a shuffled deck of 500 cards

500 Cards

What is the probability that it's actually raining in Seattle?

Raining in Seattle

What is the probability that item X would be found on Amazon's website?

Found Item

Given three uniform(0,4) random variables, what is the probability that the median of them is greater than 3?

Median Probability

Data Structures & Algorithms

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

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

Target Indices

Write a function `sorting` from scratch to sort a list of strings in ascending alphabetical order

Sort Strings

Justify an array of words given an arbitrary line width.

Max Width

Find the maximum profit from at most two stock buy/sell transactions

Maximum Profit

Product Sense & Metrics

What would you investigate to diagnose what's happening?

WAU vs Open Rates

Calculate the formula for the average lifetime value

Revenue Retention

Google Maps Improvement

How would you measure the success of Uber Eats?

Uber Eats Success

Unified Inbox

A/B Testing

How would you assess the validity of the result?

Experiment Validity

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

How would you conduct this experiment and know if the results were significant

ETA Experiment

<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>


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

<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>Amy and Brad take turns in rolling a fair six-sided die. Whoever rolls a “6” first wins the game. Amy starts by rolling first.</p>

<p>What’s the probability that Amy wins?</p>


Amy and Brad take turns in rolling a fair six-sided die. Whoever rolls a “6” first wins the game. Amy starts by rolling first.

What’s the probability that Amy wins?

<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>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>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>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>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

<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>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>


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

Download Facts

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

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

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

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

<pre tabindex="0" class="chroma"><code><span class="line"><span class="cl"><span class="n">is_balanced</span><span class="p">(</span><span class="s1">&#39;(())[]</span><span class="si">{}</span><span class="s1">&#39;</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">True</span>
</span></span></code></pre>

<pre tabindex="0" class="chroma"><code><span class="line"><span class="cl"><span class="n">is_balanced</span><span class="p">(</span><span class="s1">&#39;{([()</span><span class="si">{}</span><span class="s1">])()}&#39;</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">True</span>
</span></span></code></pre>

<pre tabindex="0" class="chroma"><code><span class="line"><span class="cl"><span class="n">is_balanced</span><span class="p">(</span><span class="s1">&#39;</span><span class="si">{}</span><span class="s1">[]())&#39;</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">False</span>
</span></span></code></pre>

<hr/>


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

The Brackets Problem

<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>Let’s say we have a very naive advertising platform. There is an audience of size <em>A</em> and a limited amount of impressions <em>B</em>.</p>

<p>Each of the impressions goes to just one user, defined at random. Every user in the audience has the same random chance of receiving each impression. </p>

<ol>
<li><p>Compute the probability that a user has exactly 0 impressions. </p></li>

<li><p>What’s the probability that every person has at least 1 impression?</p></li>
</ol>


Compute the probability a user gets exactly 0 impressions, and the probability everyone gets at least 1 impression

Impression Reach

<p>Write a Python function called <strong><code>max_profit</code></strong> that takes a list of integers, where the i-th integer represents the price of a given stock on day i, and returns the maximum profit you can achieve by buying and selling the stock.</p>

<p>You may complete, at most, two complete buy/sell transactions to maximize profits on a stock.</p>

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

<pre tabindex="0" class="chroma"><code><span class="line"><span class="cl"><span class="n">prices</span> <span class="o">=</span> <span class="p">[</span><span class="mi">3</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">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">4</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">max_profit</span><span class="p">(</span><span class="n">prices</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="mi">6</span>
</span></span></code></pre>

<p>Explanation:</p>

<pre><code>Buy on day 4 (price = 0) and sell on day 6 (price = 3): profit = 3 - 0 = 3.
Then buy again on day 7 (price = 1) and sell on day 8 (price = 4): profit = 4 - 1 = 3.
Total profit: 3 + 3 = 6.
</code></pre>


<p>Netflix has hired people to rate movies.</p>

<p>Out of all of the raters, 80% of the raters carefully rate movies and rate 60% of the movies as good and 40% as bad. The other 20% are lazy raters and rate 100% of the movies as good.</p>

<p>Assuming all raters rate the same amount of movies, what is the probability that a movie is rated good?</p>


<p>There are two categories of movie raters: careful and lazy. Careful raters make up 80% of the total. They rate 60% of the movies as good and 40% as bad. Lazy raters, constitute 20% of the raters and 100% of the movies as good. Given these parameters, calculate the overall probability of a movie being rated good.</p>

There are two categories of movie raters: careful and lazy. Careful raters make up 80% of the total. They rate 60% of the movies as good and 40% as bad. Lazy raters, constitute 20% of the raters and 100% of the movies as good. Given these parameters, calculate the overall probability of a movie being rated good.

Lazy Raters

What is the probability that a movie is rated good

<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>


Write a query to find the third purchase of every user sorted by user_id ascending

Third Purchase

<p>Given a list of <code>integers</code>, and an integer <code>N</code>, write a function <code>sum_to_n</code> to find all combinations that sum to the value <code>N</code>.</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">integers</span> <span class="o">=</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">5</span><span class="p">]</span>
</span></span><span class="line"><span class="cl"><span class="n">N</span> <span class="o">=</span> <span class="mi">8</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">sum_to_n</span><span class="p">(</span><span class="n">integers</span><span class="p">,</span> <span class="n">N</span><span class="p">)</span> <span class="o">-&gt;</span>
</span></span><span class="line"><span class="cl"> <span class="p">[</span>
</span></span><span class="line"><span class="cl">  <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">2</span><span class="p">,</span><span class="mi">2</span><span class="p">],</span>
</span></span><span class="line"><span class="cl">  <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">3</span><span class="p">],</span>
</span></span><span class="line"><span class="cl">  <span class="p">[</span><span class="mi">3</span><span class="p">,</span><span class="mi">5</span><span class="p">]</span>
</span></span><span class="line"><span class="cl"><span class="p">]</span>
</span></span></code></pre>


<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>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 you work for Lyft.</p>

<p>Lyft is testing out three variants of cancellation fees: $1, $3, and $5. How would you decide which cancellation fee to go with?</p>


How would you decide which cancellation fee to go with?

Cancellation Fees

<p>Given a table of <code>cars</code> with columns <code>id</code> and <code>make</code>, write a query that outputs a random manufacturer’s name with an equal probability of selecting any name.</p>

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

<p><code>cars</code> table</p>

<table>
<thead>
<tr>
<th><code>id</code></th>
<th><code>make</code></th>
</tr>
</thead>

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

<tr>
<td>2</td>
<td>Toyota</td>
</tr>

<tr>
<td>3</td>
<td>Toyota</td>
</tr>

<tr>
<td>4</td>
<td>Honda</td>
</tr>

<tr>
<td>5</td>
<td>Honda</td>
</tr>

<tr>
<td>6</td>
<td>Honda</td>
</tr>
</tbody>
</table>
<p><strong>Output:</strong></p>

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

<tbody>
<tr>
<td><code>make</code></td>
<td>Text</td>
</tr>
</tbody>
</table>


Write a query that outputs a random manufacturer's name with an equal probability of selecting any name.

Uniform Car Maker

<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 that you work at a bank that wants to build a model to detect fraud on the platform.</p>

<p>The bank wants to implement a text messaging service in addition that will text customers when the model detects a fraudulent transaction in order for the customer to approve or deny the transaction with a text response.</p>

<p>How would we build this model?</p>


<p>Let’s say that you work at a bank that wants to build a model to detect fraud on the platform.</p>

<p>The bank wants to implement a text messaging service that will text customers when the model detects a fraudulent transaction in order for the customer to approve or deny the transaction with a text response.</p>

<p>Which statement is true?</p>


Let's say that you work at a bank that wants to build a model to detect fraud on the platform.

The bank wants to implement a text messaging service that will text customers when the model detects a fraudulent transaction in order for the customer to approve or deny the transaction with a text response.

Which statement is true?

<p>Let’s say that you work at a B2B SAAS company that’s interested in testing the pricing of different levels of subscriptions.</p>

<p>Your project manager comes to you and asks you to run a two-week-long A/B test to test an increase in pricing.</p>

<p>How would you approach designing this test?  How would you determine whether the increase in pricing is a good business decision?</p>


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

Testing Price Increase

<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 there are six people trying to divide up into two equally separate teams. Because they want to pick random teams, on each round, each person shows their hand in either a face-up or face-down position. If there are three of each position, then they’ll split into teams. </p>

<p>What’s the expected number of rounds that everyone will have to pick a hand side before they split into teams?</p>


What’s the expected number of rounds until three people show face-up and three show face-down?

Estimated Rounds

<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>


<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>What are the <em>Z</em> and <em>t</em>-tests? What are they used for? What is the difference between them? When should use one over the other?</p>


<p>Which statistical test, the Z or t test, is more appropriate for tests with small sample sizes?</p>

Which statistical test, the Z or t test, is more appropriate for tests with small sample sizes?

Z and t Tests I

<p>What happens to the t-distribution as the degrees of freedom (ν) gets large?</p>

What happens to the t-distribution as the degrees of freedom (ν) gets large?

Z and t Tests II

What is the difference between the Z and t tests?

Z and t-Tests

<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>


How would you assess if a coin is fair after observing 8 tails in 10 flips?

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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