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

VF Corporation Interview Questions

VF Corporation Interview Guides

Machine Learning

Coefficients of Logistic Regression

Booking Regression

Lasso vs Ridge

Bagging vs Boosting

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

Softmax vs Logistic

Select the 2nd highest salary in the engineering department

2nd Highest Salary

Customer Orders

Average Order Value

Monthly Customer Report

Max Quantity

Behavioral

Strategically resolving misaligned expectations with stakeholders for a successful project outcome

Stakeholder Communication

Describing a data project and its challenges

Hurdles In Data Projects

How to present complex data insights with clarity and adaptability tailored to a specific audience

Presentations and Insights

How would you answer when an Interviewer asks why you applied to their company?

Why Do You Want to Work With Us

What do you tell an interviewer when they ask you what your strengths and weaknesses are?

Your Strengths and Weaknesses

A/B Testing

Delivery Estimate Model

Precisely ascertain whether the outcomes of an A/B test, executed to assess the impact of a landing page redesign, exhibit statistical significance.

Statistically Significant Test

How would you measure the success of a banner ad strategy?

Banner Ad Strategy Success

Delivery Online

Marketing Dollar Efficiency

Data Structures & Algorithms

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

Common Prefix

Valid Anagram

Find Mismatched Words

Given a string, write a function to determine if it is palindrome or not.

String Palindromes

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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


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

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

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

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

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

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

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

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

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

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

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

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

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

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

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


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


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

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

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

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

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

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

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

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


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

Random SQL Sample

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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


<p>As a data analyst in a fitness app company, you are tasked with analyzing the user’s daily step count.</p>

<p>Write a SQL query to calculate the 3-day rolling average of steps for each user, rounded to the nearest whole number.</p>

<p><strong><em>Notes:</em></strong> <em>The rolling average should only be calculated for days where there are 3 previous days of step counts, including the current day. The first 2 days for any user should be excluded.</em></p>

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

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

<p><code>daily_steps</code> table</p>

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

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

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

<tr>
<td>steps</td>
<td>INTEGER</td>
</tr>

<tr>
<td>date</td>
<td>DATE</td>
</tr>
</tbody>
</table>
<p><strong>Output:</strong></p>

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

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

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

<tr>
<td>avg_steps</td>
<td>FLOAT</td>
</tr>
</tbody>
</table>


Calculate the 3-day rolling average of steps for each user.

Rolling Average Steps

<p>What are the assumptions of linear regression?</p>


Which assumption of the residuals of the standard linear regression model can not be overcome by increasing the sample size?

Regression assumptions

What are the assumptions of linear regression?

Assumptions of Linear Regression

Statistics

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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


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

Black Friday Shopping Spree

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

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

<h3>Schema:</h3>

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

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

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

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

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

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

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

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

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

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

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

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

<h3>Example</h3>

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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


<p>Let’s say that you’re looking at the metrics of a job board. You see that the number of job postings per day has more or less remained the same the last few months but the number of applicants that have applied to jobs has steadily been decreasing.</p>

<p>Why would this be happening?</p>


Declining Applicants

Business Case

<p>What is the difference between covariance and correlation? Provide an example.</p>


Covariance vs Correlation

<p>Let’s say you’re given all the different marketing channels along with their respective marketing costs at a company called Mode, that sells B2B analytics dashboards.</p>

<p>What metrics would you use to determine the value of each marketing channel?</p>


<p>When determining the value of each marketing channel for the company Mode, which metric is considered the key metric?</p>

When determining the value of each marketing channel for the company Mode, which metric is considered the key metric?

Marketing Channel Metrics

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

Analytics

<p>Let’s say we want to build a model to predict booking prices on Airbnb.</p>

<p>Between linear regression and random forest regression, which model would perform better and why?</p>


<p>What are the two key concepts of random forest regression?</p>

What are the two key concepts of random forest regression?

<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>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>What’s the difference between Lasso and Ridge Regression?</p>


<p>Under what circumstances is it recommended to use Lasso regression over Ridge regression?</p>

Under what circumstances is it recommended to use Lasso regression over Ridge regression?

<p>Given a table containing data for monthly sales, write a query to find the total amount of each product sold for each month with each product as its own column in the output table.</p>

<h3>Schema</h3>

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

<p><code>monthly_sales</code> table</p>

<table>
<thead>
<tr>
<th><code>month</code></th>
<th><code>product_id</code></th>
<th><code>amount_sold</code></th>
</tr>
</thead>

<tbody>
<tr>
<td>2021-01-01</td>
<td>1</td>
<td>100</td>
</tr>

<tr>
<td>2021-01-01</td>
<td>2</td>
<td>300</td>
</tr>

<tr>
<td>2021-02-01</td>
<td>3</td>
<td>200</td>
</tr>

<tr>
<td>2021-03-01</td>
<td>4</td>
<td>250</td>
</tr>
</tbody>
</table>
<p><strong>Output:</strong></p>

<table>
<thead>
<tr>
<th><strong>month</strong></th>
<th><strong>1</strong></th>
<th><strong>2</strong></th>
<th><strong>3</strong></th>
<th><strong>4</strong></th>
</tr>
</thead>

<tbody>
<tr>
<td>2021-01-01</td>
<td>100</td>
<td>0</td>
<td>300</td>
<td>0</td>
</tr>

<tr>
<td>2021-02-01</td>
<td>0</td>
<td>200</td>
<td>0</td>
<td>0</td>
</tr>

<tr>
<td>2021-03-01</td>
<td>0</td>
<td>0</td>
<td>0</td>
<td>250</td>
</tr>
</tbody>
</table>

<h3>Example</h3>

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

<p><strong><code>monthly_sales</code> table</strong></p>

<table>
<thead>
<tr>
<th>month</th>
<th>product_id</th>
<th>amount_sold</th>
</tr>
</thead>

<tbody>
<tr>
<td>2021-01-01</td>
<td>1</td>
<td>100</td>
</tr>

<tr>
<td>2021-01-01</td>
<td>2</td>
<td>0</td>
</tr>

<tr>
<td>2021-01-01</td>
<td>3</td>
<td>250</td>
</tr>

<tr>
<td>2021-01-01</td>
<td>4</td>
<td>300</td>
</tr>

<tr>
<td>2021-02-01</td>
<td>1</td>
<td>50</td>
</tr>

<tr>
<td>2021-02-01</td>
<td>2</td>
<td>200</td>
</tr>

<tr>
<td>2021-02-01</td>
<td>3</td>
<td>0</td>
</tr>

<tr>
<td>2021-02-01</td>
<td>4</td>
<td>300</td>
</tr>

<tr>
<td>2021-03-01</td>
<td>1</td>
<td>400</td>
</tr>

<tr>
<td>2021-03-01</td>
<td>2</td>
<td>150</td>
</tr>

<tr>
<td>2021-03-01</td>
<td>3</td>
<td>200</td>
</tr>

<tr>
<td>2021-03-01</td>
<td>4</td>
<td>300</td>
</tr>

<tr>
<td>2021-04-01</td>
<td>1</td>
<td>150</td>
</tr>

<tr>
<td>2021-04-01</td>
<td>2</td>
<td>200</td>
</tr>

<tr>
<td>2021-04-01</td>
<td>3</td>
<td>50</td>
</tr>

<tr>
<td>2021-04-01</td>
<td>4</td>
<td>300</td>
</tr>

<tr>
<td>2021-05-01</td>
<td>1</td>
<td>100</td>
</tr>

<tr>
<td>2021-05-01</td>
<td>2</td>
<td>150</td>
</tr>

<tr>
<td>2021-05-01</td>
<td>3</td>
<td>0</td>
</tr>

<tr>
<td>2021-05-01</td>
<td>4</td>
<td>300</td>
</tr>
</tbody>
</table>
<p><strong>Output:</strong></p>

<table>
<thead>
<tr>
<th>month</th>
<th>1</th>
<th>2</th>
<th>3</th>
<th>4</th>
</tr>
</thead>

<tbody>
<tr>
<td>2021-01-01</td>
<td>100</td>
<td>0</td>
<td>250</td>
<td>300</td>
</tr>

<tr>
<td>2021-02-01</td>
<td>50</td>
<td>200</td>
<td>0</td>
<td>300</td>
</tr>

<tr>
<td>2021-03-01</td>
<td>400</td>
<td>150</td>
<td>200</td>
<td>300</td>
</tr>

<tr>
<td>2021-04-01</td>
<td>150</td>
<td>200</td>
<td>50</td>
<td>300</td>
</tr>

<tr>
<td>2021-05-01</td>
<td>100</td>
<td>150</td>
<td>0</td>
<td>300</td>
</tr>
</tbody>
</table>


Monthly Product Sales

<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>


<p>You’re a data scientist at YouTube. Leadership wants to revisit how the platform measures success, not for a specific new feature, but for the product as a whole given its current state.</p>

<p>How would you define and measure success for YouTube? Identify your primary and secondary metrics, explain your rationale for each, and discuss how you would choose the right thresholds for any engagement-based metrics you propose.</p>


The role of A/B testing in measuring the success rate of an analytics experiment

Success Measurement

Product Sense & Metrics

<p>Given a list of strings, write a function that returns their longest common prefix. For example, if you were given the strings <code>&#34;flowers&#34;</code>, <code>&#34;flow&#34;</code>, and <code>&#34;flight&#34;</code>, your function should return the string <code>&#34;fl&#34;</code>.</p>

<p>If the list of strings has no common prefix, return an empty string.</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">strings</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&#34;flowers&#34;</span><span class="p">,</span> <span class="s2">&#34;flow&#34;</span><span class="p">,</span> <span class="s2">&#34;flight&#34;</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="s2">&#34;fl&#34;</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">strings</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&#34;showboat&#34;</span><span class="p">,</span> <span class="s2">&#34;showcase&#34;</span><span class="p">,</span> <span class="s2">&#34;shower&#34;</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="s2">&#34;show&#34;</span>
</span></span></code></pre>


<p>Let’s say that you work for an online media company. The media company is starting to monetize its web traffic and wants to experiment with adding web banners into the middle of its reading content to see if it can monetize effectively.</p>

<p>How would you measure the success of the banner ad strategy?</p>


<p>Let’s say you’re a data scientist at LinkedIn where you’re working on a product that sends qualified job candidates to companies. The team has launched a new feature that allows candidates to message hiring managers at companies directly during the interview process to get updates on their status.</p>

<p>Due to engineering constraints, the company can’t AB test the feature before launching it.</p>

<p>How would you analyze how the feature is performing?</p>


How would you analyze how the feature is performing?

Recruiting Leads

<p>Write a function to simulate drawing balls from a jar. The colors of the balls are stored in a list named <code>jar</code>, with corresponding counts of the balls stored in the same index in a list called <code>n_balls</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">jar</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;green&#39;</span><span class="p">,</span> <span class="s1">&#39;red&#39;</span><span class="p">,</span> <span class="s1">&#39;blue&#39;</span><span class="p">]</span>
</span></span><span class="line"><span class="cl"><span class="n">n_balls</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span>
</span></span><span class="line"><span class="cl"><span class="c1"># 1 green ball</span>
</span></span><span class="line"><span class="cl"><span class="c1"># 10 red balls</span>
</span></span><span class="line"><span class="cl"><span class="c1"># and 2 blue balls</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">sample_multinomial</span><span class="p">(</span><span class="n">jar</span><span class="p">,</span> <span class="n">n_balls</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="s2">&#34;blue&#34;</span>
</span></span></code></pre>


Write code to generate a sample from a multinomial distribution with keys 

Drawing Balls From Bin

Probability

<p>Let’s say we work at L’Oréal, and the e-commerce team has noticed that about 8% of online beauty product orders are delivered late each week.</p>

<p>How would you frame this as a data science problem, choose an algorithm to address it, and describe your plan for deploying the solution?</p>


How would you solve for late deliveries using data science at L'Oréal?

Late Deliveries

Supply Chain

<p>What are the logistic and softmax functions? What is the difference between the two?</p>

<p>What makes them useful for use in logistic regression?</p>


<p>In logistic regression, how is the predicted class determined based on the logistic function?</p>


In logistic regression, how is the predicted class determined based on the logistic function?

Softmax vs Logistic II

<p>What is the key characteristic of the logistic function that makes it useful for mapping continuous values to a probability?</p>


What is the key characteristic of the logistic function that makes it useful for mapping continuous values to a probability?

Softmax vs Logistic I

<p>How would you interpret coefficients of logistic regression for categorical and boolean variables?</p>


<p>Why is one-hot encoding recommended for categorical variables in logistic regression models?</p>

Why is one-hot encoding recommended for categorical variables in logistic regression models?

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


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


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

Average Trees

Random Forest Explanation

<p>Let’s say you’re leading the analytics for a $100,000 digital marketing campaign to drive new signups for a subscription app. The campaign runs across both paid social and search channels over 30 days.</p>

<p>What metrics would you focus on to evaluate its effectiveness, and how would you use data to optimize performance during the campaign?</p>


How would you use metrics and data to evaluate and improve a digital marketing campaign?

Digital Marketing Metrics

Marketing

<p>You are given two sentences as <code>strings</code>. Create a function that returns a list of all words that are <strong>not</strong> in both sentences.</p>

<p>You can assume that the sentences have no punctuation marks, extra tabs, or spaces.</p>

<p><em>Note: The strings <code>&#34;Game&#34;</code> and <code>&#34;game&#34;</code> should be considered the same word. Because of this, make sure all the strings in the list you output are in lowercase.</em></p>

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

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

<pre tabindex="0" class="chroma"><code><span class="line"><span class="cl"><span class="n">string1</span> <span class="o">=</span> <span class="s2">&#34;I want to eat fish today with my friends&#34;</span>
</span></span><span class="line"><span class="cl"><span class="n">string2</span> <span class="o">=</span> <span class="s2">&#34;All I want is to eat fish today with my friends&#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="k">def</span> <span class="nf">mismatched_words</span><span class="p">(</span><span class="n">string1</span><span class="p">,</span><span class="n">string2</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="p">[</span><span class="s2">&#34;all&#34;</span><span class="p">,</span><span class="s2">&#34;is&#34;</span><span class="p">]</span>
</span></span></code></pre>


<p>When an interviewer asks you a question along the lines of:</p>

<ul>
<li>Why did you apply to our company?</li>
<li>What are you looking for in your next job?</li>
<li>What makes you a good fit for our company?</li>
</ul>

<p>How should you respond?</p>


<p>When asked 'What are you looking for in your next job?' in an interview, how can you tie the company's employee benefits into your response?</p>

When asked 'What are you looking for in your next job?' in an interview, how can you tie the company's employee benefits into your response?

Why Do You Want to Work With Us I

<p>How can company values be used effectively in an interview when asked 'What makes you a good fit for our company?'</p>

How can company values be used effectively in an interview when asked 'What makes you a good fit for our company?'

Why Do You Want to Work With Us II

<p>When responding to the question 'Why did you apply to our company?' during an interview, what aspect should you highlight?</p>

When responding to the question 'Why did you apply to our company?' during an interview, what aspect should you highlight?

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