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Msi workforce solutions Interview Questions

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Machine Learning

Encoding Categorical Features

Missing Housing Data

Bank Fraud Model

One Million Rides

Why would one algorithm generate different success rates with the same dataset?

Same Algorithm Different Success

Data Structures & Algorithms

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

Merge Sorted Lists

Find the missing integer from a array of consequtive integers

Find the Missing Number

Get Top N Frequent Words

Given an integer N, write a function that returns all of the prime numbers up to N

Prime to N

Move Zeros Back

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

Employee Salaries

Select the 2nd highest salary in the engineering department

2nd Highest Salary

Calculate the first touch attribution for each `user_id` that converted.

First Touch Attribution

Closest SAT Scores

Get the top 3 highest employee salaries by department

Top Three Salaries

Probability

First to Six

500 Cards

Raining in Seattle

Found Item

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

Median Probability

Statistics

Say you are given a dataset of perfectly linearly separable data. What would happen when you run logistic regression?

Perfectly Separable

P-value to a Layman

What are the assumptions of linear regression?

Assumptions of Linear Regression

Using R Squared

What statistical test could you use to determine which of two parcel types is better to use, given how often they are damaged?

Different Parcel Effectiveness

Write a SQL query to select the 2nd highest salary in the engineering department.

Note: If more than one person shares the highest salary, the query should select the next highest salary.

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

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>

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

Note: The output should include the full name of the employee in one column, the department name, and the salary. The output should be sorted by department name in ascending order and salary in descending order. 

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>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>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>employee_nam</code>e</td>
<td>VARCHAR</td>
</tr>

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

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

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>

Manager Team Sizes

The schema below is for a retail online shopping company consisting of two tables, <code>attribution</code> and <code>user_sessions</code>.

<ul>
<li>The attribution table logs a session visit for each row.</li>

<li>If <code>conversion</code> is <code>true</code>, then the user converted to buying on that session.</li>

<li>The <code>channel</code> column represents which advertising platform the user was attributed to for that specific session.</li>

<li>Lastly the <code>user_sessions</code> table maps many to one session visits back to one user.</li>
</ul>

First touch attribution is defined as the channel with which the converted user was associated when they first discovered the website.

Calculate the first touch attribution for each <code>user_id</code> that converted. 

Example:

Input:

<code>attribution</code> table

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

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

<tr>
<td><code>channel</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>conversion</code></td>
<td>BOOLEAN</td>
</tr>
</tbody>
</table>
<code>user_sessions</code> table

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

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

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

<tr>
<td><code>user_id</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
Example output:

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

<tbody>
<tr>
<td>123</td>
<td>facebook</td>
</tr>

<tr>
<td>145</td>
<td>google</td>
</tr>

<tr>
<td>153</td>
<td>facebook</td>
</tr>

<tr>
<td>172</td>
<td>organic</td>
</tr>

<tr>
<td>173</td>
<td>email</td>
</tr>
</tbody>
</table>

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?

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?

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

Bonus: What’s the time complexity?

Example:

Input:

<pre tabindex="0" class="chroma"><code>list1 = [1,2,5]
list2 = [2,4,6]
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>def merge_list(list1,list2) -&gt; [1,2,2,4,5,6]
</code></pre>

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

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

Merging Lists Complexity

You have an array of integers, <code>nums</code> of length <code>n</code> spanning <code>0</code> to <code>n</code> with one missing. Write a function <code>missing_number</code> that returns the missing number in the array.

Note: Complexity of \(O(n)\) required.

Example:

Input:

<pre tabindex="0" class="chroma"><code>nums = [0,1,2,4,5] 
missing_number(nums) -&gt; 3
</code></pre>

Given an integer <code>N</code>, write a function that returns a list of all of the prime numbers up to <code>N</code>.

Note: Return an empty list there are no prime numbers less than or equal to <code>N</code>.

Example:

Input:

<pre tabindex="0" class="chroma"><code>N = 3
</code></pre>

Output:

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

Let’s say you’re working in HR at a major tech company, and they ask you to find employees with a high probability of leaving the company. They tell you these employees perform well but have the lowest pay.

Given two tables, <code>employees</code> and <code>projects</code>, find the five lowest-paid employees who have completed at least three projects.

Note: We consider projects to be completed when they have an end date, which is the same as saying their <code>End_dt</code> is not <code>NULL</code>.

Example:

Input:

<code>employees</code> table

<table>
<thead>
<tr>
<th>id</th>
<th>salary</th>
</tr>
</thead>

<tbody>
<tr>
<td>INTEGER</td>
<td>FLOAT</td>
</tr>
</tbody>
</table>
<code>projects</code> table

<table>
<thead>
<tr>
<th>employee_id</th>
<th>project_id</th>
<th>Start_dt</th>
<th>End_dt</th>
</tr>
</thead>

<tbody>
<tr>
<td>INTEGER</td>
<td>INTEGER</td>
<td>DATETIME</td>
<td>DATETIME</td>
</tr>
</tbody>
</table>
Output:

<table>
<thead>
<tr>
<th>employee_id</th>
</tr>
</thead>

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

Find the five employees with the hightest probability of leaving the company

Top 5 Turnover Risk

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

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

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

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

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

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

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

Largest Salary by Department

There are two tables. One table is called <code>swipes</code> that holds a row for every Tinder swipe and contains a boolean column that determines if the swipe was a right or left swipe called <code>is_right_swipe</code>. The second is a table named <code>variants</code> that determines which user has which variant of an AB test.

Write a SQL query to output the average number of right swipes for two different variants of a feed ranking algorithm by comparing users that have swiped 10, 50, and 100 swipes in a <code>feed_change</code> experiment.

Note: Users have to have swiped at least 10 times to be included in the subset of users to analyze the mean number of right swipes.

Example:

Input:

<code>variants</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>experiment</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>variant</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>user_id</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
<code>swipes</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>swiped_user_id</code></td>
<td>INTEGER</td>
</tr>

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

<tr>
<td><code>is_right_swipe</code></td>
<td>BOOLEAN</td>
</tr>
</tbody>
</table>
Output:

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

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

<tr>
<td><code>mean_right_swipes</code></td>
<td>FLOAT</td>
</tr>

<tr>
<td><code>swipe_threshold</code></td>
<td>INTEGER</td>
</tr>

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

Write a SQL query to find the average number of right swipes for different ranking algorithms.

Swipe Precision

A/B Testing

We’re given two tables. One is named <code>projects</code> and the other maps employees to the projects they’re working on. 

We want to select the five most expensive projects by budget to employee count ratio. But let’s say that we’ve found a bug where there exist duplicate rows in the <code>employee_projects</code> table.

Write a query to account for the error and select the top five most expensive projects by budget to employee count ratio.

<h3>Schema</h3>

Input:

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

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

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

<tr>
<td><code>budget</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
<code>employee_projects</code> table

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

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

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

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

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

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

<h3>Example</h3>

Input:

<code>projects</code> table

<table>
<thead>
<tr>
<th>id</th>
<th>title</th>
<th>start_date</th>
<th>end_date</th>
<th>budget</th>
</tr>
</thead>

<tbody>
<tr>
<td>1</td>
<td>party</td>
<td>2006-04-17 00:00:00</td>
<td>2006-05-19 00:00:00</td>
<td>25875</td>
</tr>

<tr>
<td>2</td>
<td>diversity</td>
<td>2017-08-09 00:00:00</td>
<td>2017-09-18 00:00:00</td>
<td>77867</td>
</tr>

<tr>
<td>3</td>
<td>integration</td>
<td>2005-06-29 00:00:00</td>
<td>2005-07-19 00:00:00</td>
<td>75987</td>
</tr>

<tr>
<td>4</td>
<td>testing</td>
<td>2009-01-12 00:00:00</td>
<td>2009-05-03 00:00:00</td>
<td>35946</td>
</tr>

<tr>
<td>5</td>
<td>launch</td>
<td>2005-05-02 00:00:00</td>
<td>2005-10-16 00:00:00</td>
<td>66292</td>
</tr>

<tr>
<td>6</td>
<td>meet</td>
<td>2001-09-09 00:00:00</td>
<td>2002-04-04 00:00:00</td>
<td>71243</td>
</tr>

<tr>
<td>7</td>
<td>payroll</td>
<td>2019-09-25 00:00:00</td>
<td>2020-05-05 00:00:00</td>
<td>97071</td>
</tr>

<tr>
<td>8</td>
<td>admin</td>
<td>2000-07-08 00:00:00</td>
<td>2000-09-10 00:00:00</td>
<td>24000</td>
</tr>

<tr>
<td>9</td>
<td>petronas</td>
<td>2016-07-01 00:00:00</td>
<td>2016-11-28 00:00:00</td>
<td>37088</td>
</tr>

<tr>
<td>10</td>
<td>bel</td>
<td>2005-08-03 00:00:00</td>
<td>2006-04-29 00:00:00</td>
<td>61937</td>
</tr>
</tbody>
</table>
<code>employee_projects</code> table

<table>
<thead>
<tr>
<th>project_id</th>
<th>employee_id</th>
</tr>
</thead>

<tbody>
<tr>
<td>1</td>
<td>7</td>
</tr>

<tr>
<td>1</td>
<td>9</td>
</tr>

<tr>
<td>2</td>
<td>6</td>
</tr>

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

<tr>
<td>3</td>
<td>2</td>
</tr>

<tr>
<td>4</td>
<td>5</td>
</tr>

<tr>
<td>5</td>
<td>3</td>
</tr>

<tr>
<td>5</td>
<td>4</td>
</tr>

<tr>
<td>6</td>
<td>8</td>
</tr>

<tr>
<td>8</td>
<td>10</td>
</tr>

<tr>
<td>9</td>
<td>11</td>
</tr>

<tr>
<td>10</td>
<td>12</td>
</tr>

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

<tr>
<td>9</td>
<td>11</td>
</tr>

<tr>
<td>6</td>
<td>8</td>
</tr>

<tr>
<td>4</td>
<td>5</td>
</tr>
</tbody>
</table>
Output:

<table>
<thead>
<tr>
<th>title</th>
<th>budget_per_employee</th>
</tr>
</thead>

<tbody>
<tr>
<td>diversity</td>
<td>77867</td>
</tr>

<tr>
<td>meet</td>
<td>71243</td>
</tr>

<tr>
<td>bel</td>
<td>61937</td>
</tr>

<tr>
<td>integration</td>
<td>37993.5</td>
</tr>

<tr>
<td>petronas</td>
<td>37088</td>
</tr>
</tbody>
</table>

Project Budget Error

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

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. 

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

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

Impression Reach

Given a list of integers, identify all the duplicate values in the list. Assume that the list can contain both positive and negative numbers, and the order of the list does not matter. A number is considered a duplicate if it appears more than once in the list. Return a list of the duplicate numbers.

Example 1:

Input:

<pre tabindex="0" class="chroma"><code>nums = [1, 2, 3, 1, 2, 3]
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>find_duplicates(nums) -&gt; [1, 2, 3]
</code></pre>

The numbers 1, 2, and 3 all appear more than once in the list, so they are considered duplicates.

Example 2:

Input:

<pre tabindex="0" class="chroma"><code>nums = [1, -1, 2, 3, 3, -1]
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>find_duplicates(nums) -&gt; [-1, 3]
</code></pre>

The numbers -1 and 3 both appear more than once in the list, so they are considered duplicates. Note that the order of the output does not matter.

Example 3:

Input:

<pre tabindex="0" class="chroma"><code>nums = [1, 2, 3, 4, 5]
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>find_duplicates(nums) -&gt; []
</code></pre>

None of the numbers in the list appear more than once, so there are no duplicates.

This problem involves identifying duplicate numbers in a list of integers. The function should return a list of the duplicate numbers.

Find Duplicate Numbers in a List

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

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

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

Example:

<pre tabindex="0" class="chroma"><code>is_balanced(&#39;(())[]{}&#39;) -&gt; True
</code></pre>

<pre tabindex="0" class="chroma"><code>is_balanced(&#39;{([(){}])()}&#39;) -&gt; True
</code></pre>

<pre tabindex="0" class="chroma"><code>is_balanced(&#39;{}[]())&#39;) -&gt; False
</code></pre>

<hr/>

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

The Brackets Problem

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>

Google Maps Improvement

Product Sense & Metrics

Let’s say you run a wine house. You have detailed information about the chemical composition of wines in a <code>wines</code> table.

One day, a customer comes asking specifically for a wine that has

<ul>
<li>Greater or equal to than 13% alcohol content</li>
<li>Ash content less than 2.4</li>
<li>A color intensity less than 3</li>
</ul>

Write a query to find the <code>id</code>s of suitable wines for this customer.

Note: All percentages are reported with two numbers before the decimal point; for example, 13.55% is represented as <code>13.55</code> instead of <code>0.1355</code>.

Example:

Input:

<code>wines</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>alcohol</code></td>
<td>FLOAT</td>
</tr>

<tr>
<td><code>malic_acid</code></td>
<td>FLOAT</td>
</tr>

<tr>
<td><code>ash</code></td>
<td>FLOAT</td>
</tr>

<tr>
<td><code>alcalinity_of_ash</code></td>
<td>FLOAT</td>
</tr>

<tr>
<td><code>magnesium</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>total_phenols</code></td>
<td>FLOAT</td>
</tr>

<tr>
<td><code>flavanoids</code></td>
<td>FLOAT</td>
</tr>

<tr>
<td><code>nonflavanoid_phenols</code></td>
<td>FLOAT</td>
</tr>

<tr>
<td><code>proanthocyanins</code></td>
<td>FLOAT</td>
</tr>

<tr>
<td><code>color_intensity</code></td>
<td>FLOAT</td>
</tr>

<tr>
<td><code>hue</code></td>
<td>FLOAT</td>
</tr>

<tr>
<td><code>od280_or_od315_of_diluted_wines</code></td>
<td>FLOAT</td>
</tr>

<tr>
<td><code>proline</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>

Let’s say you run a wine house. You have detailed information about the chemical composition of wines in a wines table.

SELECTive Wine Connoisseur

Say you are given a dataset of perfectly linearly separable data.

What would happen when you run logistic regression?

What happens when logistic regression is run on a dataset of perfectly linearly separable data?

What happens when logistic regression is run on a dataset of perfectly linearly separable data?

Say you are tasked with designing a data mart or data warehouse for a new online retailer.

How would you design the system?

Note: Sketch a star schema to explain your design.

Design a data warehouse for a new online retailer

Retailer Data Warehouse

Data Modeling

Given an array and a target integer, write a function <code>sum_pair_indices</code> that returns the indices of two integers in the array that add up to the target integer. If not found, just return an empty list.

Note: Can you do it on \(O(n)\) time?

Note: Even though there could be many solutions, only one needs to be returned.

Example 1:

Input:

<pre tabindex="0" class="chroma"><code>array = [1 2 3 4] 
target = 5 
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>def sum_pair_indices(array, target) -&gt; [0 3] or [1 2]
</code></pre>

Example 2:

Input:

<pre tabindex="0" class="chroma"><code>array = [3]
target = 6 
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>Do NOT return [0 0] as you can&#39;t use an index twice.
</code></pre>

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 Python function called <code>longest_increasing_subsequence</code> that takes a list of integers as input and returns as output the length of the longest increasing subsequence in the list.

Note: An increasing subsequence is a subset of a list where the elements are in strictly increasing order. The subsequence does not have to be contiguous or unique.

<h3>Example:</h3>

Input:

<pre tabindex="0" class="chroma"><code>nums = [10, 9, 2, 5, 3, 3, 7, 101, 18]
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>4
</code></pre>

Explanation:

The longest increasing subsequence is <code>[2, 3, 7, 101]</code>, for a total length of 4.

Find the longest increasing subsequence in a list of integers.

Longest Increasing Subsequence

Why would the same machine learning algorithm generate different success rates using the same dataset?

Note: When they ask us an ambiguous question, we need to gather context and restate it in a way that’s clear for us to answer.

What are the assumptions of linear regression?

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

Regression assumptions

Suppose there are one million users and we want to expose 1000 users per day to a test. The same user can be selected twice for the test.

<ol>
<li>What’s the expected value of how long someone will have to wait before they receive the test?</li>

<li>What is the likelihood they get selected after the first day? Is that closer to 0 or 1?</li>
</ol>

Suppose there are one million users and we want to expose 1000 users per day to a test. The same user can be selected twice for the test.What's the expected value of how long someone will have to wait before they receive the test?

Suppose there are one million users and we want to expose 1000 users per day to a test. The same user can be selected twice for the test.What's the expected value of how long someone will have to wait before they receive the test?

Expected Tests

Three zebras are chilling in the desert. Suddenly a lion attacks.
Each zebra is sitting on a corner of a triangle with sides of equal length.
Each zebra randomly picks a direction and only runs along the outline of the triangle to either edge of the triangle.

What is the probability that none of the zebras collide?

Three zebras are chilling in the desert. Suddenly a lion attacks.

Each zebra is sitting on a corner of a triangle with side of equal length. Each zebra randomly picks a direction and only runs along the outline of the triangle to either edge of the triangle.

What is the probability that none of the zebras collide?

Three zebras are chilling in the desert. Suddenly a lion attacks.

Each zebra is sitting on a corner of a triangle with side of equal length.  Each zebra randomly picks a direction and only runs along the outline of the triangle to either edge of the triangle.

What is the probability that none of the zebras collide?



Three Zebras

Brainteasers

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.

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

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

Fair Coin

Given three random variables independently and identically distributed from a uniform distribution of 0 to 4, what is the probability that the median of them is greater than 3?

How can we calculate the probability that the median of three random variables from a uniform distribution of 0 to 4 is greater than 3?

How can we calculate the probability that the median of three random variables from a uniform distribution of 0 to 4 is greater than 3?

Given a 2-D matrix P of predicted values and actual values, write a function precision_recall to calculate precision and recall metrics.

Return the ordered pair (precision, recall).

Example:

Input:

<pre tabindex="0" class="chroma"><code>
P = [[121, 9],
 [17, 144]]
</code></pre>

where P represents the following table

<table>
<thead>
<tr>
<th></th>
<th>Predicted =TRUE</th>
<th>Predicted =FALSE</th>
</tr>
</thead>

<tbody>
<tr>
<td>Actual =TRUE</td>
<td>121</td>
<td>9</td>
</tr>

<tr>
<td>Actual =FALSE</td>
<td>17</td>
<td>144</td>
</tr>
</tbody>
</table>
Output:

<pre tabindex="0" class="chroma"><code>def precision_recall(P) -&gt; (0.931, 0.877,)
</code></pre>

Write a function to calculate precision and recall metrics.

Precision and Recall

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>

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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