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Joby Aviation Interview Questions

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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 you build the recommendation algorithm for type-ahead search for Netflix

Type-ahead Search

How to model merchant acquisition in a new market?

Merchant Acquisition

What kind of model would we build and what features would we use

Food Prep Features

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

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

Binary Tree Conversion

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

Manager Team Sizes

Distance Traveled

User Experience Percentage

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

Download Facts

A/B Testing

How would you assess the validity of the result?

Experiment Validity

How do we go about selecting the best 10,000 customers for the pre-launch?

Pre-Launching Shows

Button AB Test

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

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

What would you investigate to figure out what happened

Increased Cancellations

Let’s say you work at a file-hosting website. You have information on user’s daily downloads in the <code>download_facts</code> table

Use the window function <code>RANK</code> to display the top three users by downloads each day. Order your data by <code>date</code>, and then by <code>daily_rank</code>

Example:

Input:

<code>download_facts</code> table

<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>date</code></td>
<td>DATE</td>
</tr>

<tr>
<td><code>downloads</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
Output:

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

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

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

<tr>
<td><code>date</code></td>
<td>DATE</td>
</tr>

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

Write a query to display the top three users by downloads each day

Top 3 Users

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.

Example:

Input:

<code>employees</code> table

<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>
<code>departments</code> table

<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>
Output:

<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>

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.

How would you assess the validity of the result?

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.

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

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

Example:

Input:

<code>accounts</code> table

<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>
<code>downloads</code> table

<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>
Output:

<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>

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>.

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.

Example:

Input:

<code>user_experiences</code> table

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

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

<tr>
<td><code>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>
Output:

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

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

Write a query to identify the manager with the biggest team size.

You may assume there is only one manager with the largest team size.

Example:

Input:

<code>employees</code> table

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

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

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

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

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

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

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

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

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

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

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

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

Example:

Input:

<code>users</code> table

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

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

<tr>
<td><code>name</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
<code>rides</code> table

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

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

<tr>
<td><code>passenger_user_id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>distance</code></td>
<td>FLOAT</td>
</tr>
</tbody>
</table>
Output:

<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>

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.

Example 1:

Input:

<pre tabindex="0" class="chroma"><code>weights = {&#39;A&#39;: 1, &#39;B&#39;: 2}
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>random_key(weights) -&gt; return A 1/3 of the time, B 2/3 of the time
</code></pre>

Example 2:

Input:

<pre tabindex="0" class="chroma"><code>weights = {&#39;A&#39;: 1, &#39;B&#39;: 1}
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>random_key(weights) -&gt; return A 1/2 of the time, B 1/2 of the time
</code></pre>

Weighted Keys

Probability

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

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.

How would you set up this test?

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.

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

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

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.

Example:

Input:

<code>transactions</code> table

<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>
Output:

<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

Write a Python function called <code>max_profit</code> 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.

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

Input:

<pre tabindex="0" class="chroma"><code>prices = [3, 3, 5, 0, 0, 3, 1, 4]
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>def max_profit(prices) -&gt; 6
</code></pre>

Explanation:

<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>

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 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.

How would we build this model?

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?

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?

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

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

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

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?

Describing a data project and its challenges

Hurdles In Data Projects

Behavioral

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%.

What would you investigate to diagnose what’s happening?

Email Open Rate

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>.

Example: 

Input:

<pre tabindex="0" class="chroma"><code>integers = [2,3,5]
N = 8
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>def sum_to_n(integers, N) -&gt;
 [
 [2,2,2,2],
 [2,3,3],
 [3,5]
]
</code></pre>

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

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

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

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

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?

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

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

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

Network Experiment Design III

Let’s say that you work for a software as a subscription (SAAS) company that has existed for just over a year. The chief revenue officer wants to know the average lifetime value.

We know that the product costs $100 dollars per month, averages 10% in monthly churn, and the average customer sticks around for around 3.5 months.

Calculate the formula for the average lifetime value.

If a software-as-a-subscription company has a monthly churn rate of 20% for a product that costs $100, what would be the average lifetime value of a single customer?

If a software-as-a-subscription company has a monthly churn rate of 20% for a product that costs $100, what would be the average lifetime value of a single customer?

Average Lifetime Value

Let’s say you work at Allstate. Allstate is running <code>N</code> online ads right now. The table <code>ads</code> contains all those ads, ranked by popularity via the <code>id</code> column (e.g., the entry with <code>id = 1</code> is the most popular, etc.).

Create a subquery or common table expression named <code>top_ads</code> containing the top 3 ads (by popularity) and return the number of rows that would result from the following operations

<ol>
<li><code>ads INNER JOIN top_ads</code></li>
<li><code>ads LEFT JOIN top_ads</code></li>
<li><code>ads RIGHT JOIN top_ads</code></li>
<li><code>ads CROSS JOIN top_ads</code></li>
</ol>

Note: Please make the <code>join_type</code> column in your output have the values <code>inner_join</code>, <code>left_join</code>, etc. for each of their respective join types

Note: Please return only one query with each number in a different row

Example:

Input:

<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>
Output:

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

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

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

Create top_ads with the top 3 ads and return the row counts for inner, left, right, and cross joins with ads

Size of Joins

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

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

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

P-value to a Layman I

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

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

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.

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.

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.

For each recipe, compute:

<ol>
<li><code>missing_ingredients</code>: number of ingredients the family does not already have in their pantry</li>
<li><code>min_total_cost</code>: 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>

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

Schema

Input

<code>recipes</code> Table

<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>
<code>ingredient_brands</code> Table

<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>
<code>pantry</code> Table

<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>
<code>acceptable_brands</code> Table

<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>
Output

<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>
Example

Input

<code>recipes</code> Table

<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>
<code>pantry</code> Table

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

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

<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>
<code>acceptable_brands</code> Table

<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>
Output

<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>Explanation</h3>

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.

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.

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

Christmas Dinner Ingredient Optimization

Build a $k$ Nearest Neighbors classification model from scratch with the following conditions:

<ul>
<li>Use Euclidian distance (aka, the “2 norm”) as your closeness metric</li>
<li>Your function should be able to handle data frames of arbitrary many rows and columns</li>
<li>If there is a tie in the class of the $k$ nearest neighbors, rerun the search using $k-1$ neighbors instead</li>
<li>You may use <code>pandas</code> and <code>numpy</code> but NOT <code>scikit-learn</code></li>
</ul>

Example:

Input:

<pre tabindex="0" class="chroma"><code>k = 5
new_point = [0.5,-2,8]
print(data)
...
 Var1 Var2 Var3 Target
0 -3.279536 3.362223 2.847892 2
1 -0.791565 1.742475 2.151587 2
2 -0.785992 -0.938681 -0.459770 0
3 -1.068190 1.461051 0.127130 3
4 -0.367568 -0.870240 -0.225734 0
.. ... ... ... ...
95 -1.327175 1.971085 -0.690689 2
96 -3.203714 1.847649 0.778901 2
97 -0.587640 0.647458 2.094385 2
98 0.363644 -0.509795 2.514191 1
99 -0.673498 2.955285 2.102122 4

[100 rows x 4 columns]
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>def kNN(k, new_point, data) -&gt; 2
</code></pre>

Build a k Nearest Neighbors classification model from scratch.

KNN From Scratch

Let’s say that you’re the PM on Google Maps.

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

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

Write a Python function called <code>group_anagrams</code> that takes a list of strings as input and returns a list of lists, where each inner list contains a group of anagrams that were present in the input list.

An anagram is a word or phrase formed by rearranging the letters of a different word or phrase, using all the original letters exactly once.

Note: Each of the inner lists should be ordered alphabetically and contain no duplicate values

<h3>Example:</h3>

Input:

<pre tabindex="0" class="chroma"><code>words = [&#34;eat&#34;, &#34;tea&#34;, &#34;tan&#34;, &#34;ate&#34;, &#34;nat&#34;, &#34;bat&#34;]
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>[ 
 [&#34;nat&#34;,&#34;tan&#34;],
 [&#34;ate&#34;,&#34;eat&#34;, &#34;tea&#34;],
 [&#34;bat&#34;]
]
</code></pre>

Explanation:

In the example, the words “eat”, “tea”, and “ate” are anagrams of each other, as are the words “tan” and “nat”. The word “bat” does not have any anagrams in the input list, and therefore is listed on its own. The function then groups the anagrams together and returns a list of lists representing these groups.

Each of the lists representing a group of anagrams is sorted in alphabetical order.

Identify the groups of anagrams in a list of words

Groups of Anagrams

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

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

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

Average Trees

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

Random Forest Explanation

How would you build the recommendation algorithm for type-ahead search for Netflix?

Let’s say we want to improve the matching algorithm for drivers and riders for Uber. The engineering team has added a new column to the <code>drivers</code> table called <code>weighting</code>. It contains a weighted value, which they hope will lead to better matching.

Given this table of drivers, write a query to perform a weighted random selection of a driver based on the driver weight.

Example:

Input:

<code>drivers</code> table

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

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

<tr>
<td><code>weighting</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
Output:

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

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

Select a (weight) random driver from the database.

Random Weighted Driver

Let’s say you work at Uber. 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?

Note: Assume you have user location data but not external data sources.

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

Suppose there exists a new airline named Jetco that flies domestically across North America. Jetco recently had a study commissioned that tested the boarding time of every airline, and it came out that Jetco had the fastest average boarding times of any airline.

What factors could have biased this result, and what would you look into?

What could potentially bias the average boarding time in favor of an airline that only flies domestically?

What could potentially bias the average boarding time in favor of an airline that only flies domestically?

Boarding Times Bias

A new airline came out as the fastest average boarding times compared to other airlines.  What factors could have biased this result and what would you look into?

Given a list of strings, write a function, <code>sorting</code> from scratch to sort the list in ascending alphabetical order.

Notes:
- Do NOT use the built-in <code>sorted</code> function
- Return the new sorted list, rather than modifying the list in-place

Bonus: Have your solution be $O(n log(n))$.

Example:

Input:

<pre tabindex="0" class="chroma"><code>array = [&#34;apple&#34;, &#34;cat&#34;, &#34;banana&#34;, &#34;zoo&#34;, &#34;football&#34;]
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>def sorting(array) -&gt; [&#34;apple&#34;, &#34;banana&#34;, &#34;cat&#34;, &#34;football&#34;, &#34;zoo&#34;]
</code></pre>

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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