Prepare for and practice interview questions from Bohemia Interactive Simulations.

Bohemia Interactive Simulations Interview Questions

Bohemia Interactive Simulations Interview Guides

Machine Learning

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

Same Algorithm Different Success

Bagging vs Boosting

Loan Model

Spam Classifier 

Decision Tree Evaluation

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

One Element Removed

String Mapping

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

A/B Testing

Button AB Test

Green Dot

Network Experiment Design

Testing Price Increase

New UI Effect

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

Rolling Bank Transactions

Project Budget Error

Employee Project Budgets

Product Sense & Metrics

Group Success

WAU vs Open Rates

Google Maps Improvement

Unified Inbox

Stories Success

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>

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>

We’re given a table of bank transactions with three columns, <code>user_id</code>, a deposit or withdrawal value (determined if the value is positive or negative), and <code>created_at</code> time for each transaction.

Write a query to get the total three-day rolling average for deposits by day.

Note: Please use the format <code>&#39;%Y-%m-%d&#39;</code> for the date in the outout

Example:

Input:

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

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

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

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

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

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

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

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>

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>

Write a function <code>compute_deviation</code> that takes in a list of dictionaries with a key and list of integers and returns a dictionary with the standard deviation of each list.

Note: This should be done without using the NumPy built-in functions.

Example:

Input:

<pre tabindex="0" class="chroma"><code>input = [
 {
 &#39;key&#39;: &#39;list1&#39;,
 &#39;values&#39;: [4,5,2,3,4,5,2,3],
 },
 {
 &#39;key&#39;: &#39;list2&#39;,
 &#39;values&#39;: [1,1,34,12,40,3,9,7],
 }
]
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code> output = {&#39;list1&#39;: 1.12, &#39;list2&#39;: 14.19}
</code></pre>

Compute Deviation

Statistics

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

Write a query to get the top five most expensive projects by budget to employee count ratio.

Note: Exclude projects with 0 employees. Assume each employee works on only one project.

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

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

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

P-value to a Layman

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

There are two lists, <code>list X</code> and <code>list Y</code>. Both lists contain integers from <code>-1000</code> to <code>1000</code> and are identical to each other except that one integer is removed in <code>list Y</code> that exists in <code>list X</code>.

Write a function <code>one_element_removed</code> that takes in both lists and returns the integer that was removed in $O(1)$ space and $O(n)$ time without using the python set function.

Example:

Input:

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

one_element_removed(list_x, list_y) -&gt; 3
</code></pre>

Which of the following methods would be the most efficient way of identifying what element is not included in two lists of integers that differ by a single element being removed?

For example, if the lists are <code>[1,2,3,4,5]</code> and <code>[1,2,4,5]</code>, the function should return <code>3</code>

Which of the following methods would be the most efficient way of identifying what element is not included in two lists of integers that differ by a single element being removed? 

For example, if the lists are `[1,2,3,4,5]` and `[1,2,4,5]`, the function should return `3`

Efficiently Identifying Missing Integer

Let’s say you are designing a marketplace for your website.

Selling firearms is prohibited by your website’s Terms of Service Agreement (not to mention the laws of your country). To this end, you want to create a system that can automatically detect if a listing on the marketplace is selling a gun.

How would you go about doing this?

While designing a system to detect firearms listings on a marketplace website, what metrics could be used to minimize both false positives and false negatives?

While designing a system to detect firearms listings on a marketplace website, what metrics could be used to minimize both false positives and false negatives?

Detecting Firearm Sales

How would you build a model to detect if a post on a marketplace is talking about selling a gun?

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 that $X$ and $Y$ are independent random variables with normal distributions, what is the mean and variance of the distribution of $2X - Y$ when the corresponding distributions are $X\sim \mathcal{N}(3, 4)$ and $Y \sim \mathcal{N}(1, 4)$?

Given that $X$ and $Y$ are independent random variables with normal distributions, and the corresponding distributions are $X\sim \mathcal{N}(3, 4)$ and $Y \sim \mathcal{N}(1, 4)$, what is the mean of the distribution of $2X - Y$?

Given that $X$ and $Y$ are independent random variables with normal distributions, and the corresponding distributions are $X\sim \mathcal{N}(3, 4)$ and $Y \sim \mathcal{N}(1, 4)$, what is the mean of the distribution of $2X - Y$?

Distribution of 2X - Y I

Given that $X$ and $Y$ are independent random variables with normal distributions, and the corresponding distributions are $X\sim \mathcal{N}(3, 4)$ and $Y \sim \mathcal{N}(1, 4)$, what is the variance of the distribution of $2X - Y$?

Given that $X$ and $Y$ are independent random variables with normal distributions, and the corresponding distributions are $X\sim \mathcal{N}(3, 4)$ and $Y \sim \mathcal{N}(1, 4)$, what is the variance of the distribution of $2X - Y$?

Distribution of 2X - Y II

Distribution of 2X - Y

Probability

You are given a binary tree of unique positive numbers. Each node in the binary tree is represented as a dictionary with the following keys:

<ul>
<li><code>data</code>: integer value stored in the node</li>
<li><code>left</code>: reference to the left child (or <code>None</code>)</li>
<li><code>right</code>: reference to the right child (or <code>None</code>)</li>
</ul>

<pre tabindex="0" class="chroma"><code>node = {
 &#34;data&#34;: 6,
 &#34;left&#34;: {
 &#34;data&#34;: 3,
 &#34;left&#34;: {...},
 &#34;right&#34;: {...}
 },
 &#34;right&#34;: {
 &#34;data&#34;: 9,
 &#34;left&#34;: {...},
 &#34;right&#34;: {...}
 }
}
</code></pre>

Given two node values as input (<code>value1</code> and <code>value2</code>), write a function to return the value of the nearest common ancestor (lowest node in the tree that has both nodes as descendants).

Note: If one of the nodes doesn’t exist in the tree, return <code>-1</code>.

Example:

Input:

<pre tabindex="0" class="chroma"><code># Diagram of the binary tree
&#39;&#39;&#39;
 6 
 / \ 
 3 9 
 / \
 2 11
 / \
 5 8
&#39;&#39;&#39;
value1 = 8
value2 = 2
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>common_ancestor(root,value1,value2) -&gt; 3
</code></pre>

Explanation:

<ul>
<li>Ancestors of <code>8</code>: <code>11 → 3 → 6</code></li>
<li>Ancestors of <code>2</code>: <code>3 → 6</code></li>
<li>Common ancestors: <code>3</code> and <code>6</code></li>
<li>The nearest common ancestor is <code>3</code>.</li>
</ul>

Write a function to return the value of the nearest node that is a parent to both nodes.

Nearest Common Ancestor

Given two strings, <code>string1</code> and <code>string2</code>, write a function <code>str_map</code> to determine if there exists a one-to-one correspondence (bijection) between the characters of <code>string1</code> and <code>string2</code>.

For the two strings, our correspondence must be between characters in the same position/index.

Example 1:

Input:

<pre tabindex="0" class="chroma"><code>string1 = &#39;qwe&#39;
string2 = &#39;asd&#39;

string_map(string1, string2) == True

# q = a, w = s, and e = d
</code></pre>

Example 2:

Input:

<pre tabindex="0" class="chroma"><code>string1 = &#39;donut&#39;
string2 = &#39;fatty&#39;

string_map(string1, string2) == False
# cannot map two distinct characters to two equal characters
</code></pre>

Example 3:

Input:

<pre tabindex="0" class="chroma"><code>string1 = &#39;enemy&#39;
string2 = &#39;enemy&#39;

string_map(string1, string2) == True
# there exists a one-to-one correspondence between equivalent strings
</code></pre>

Example 4:

Input:

<pre tabindex="0" class="chroma"><code>string1 = &#39;enemy&#39;
string2 = &#39;ymene&#39;

string_map(string1, string2) == False
# since our correspondence must be between characters of the same index, this case returns &#39;False&#39; as we must map e = y AND e = e
</code></pre>

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>

How would you measure the success of Facebook Groups?

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

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

You’re an analyst at Ubisoft Gaming. Leadership wants to track Weekly Active Paying Users (WAPU), players who record at least one game session and at least one in-app purchase in the same calendar week.

Write a SQL query that returns, for each week in the dataset:

<ol>
<li><code>week_start</code>- the Monday of that calendar week 
</li>
<li><code>wapu</code>- the number of distinct users who qualify as WAPU in that week 
</li>
</ol>

Note: Assume all timestamps are in UTC. Output should be ordered by <code>week_start</code> ascending.

<h3>Schema:</h3>

Input:

<code>game_sessions</code> table

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

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

<tr>
<td>session_id</td>
<td>INTEGER</td>
</tr>

<tr>
<td>session_start</td>
<td>TIMESTAMP</td>
</tr>

<tr>
<td>country</td>
<td>VARCHAR</td>
</tr>
</tbody>
</table>
<code>purchases</code> table

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

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

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

<tr>
<td>purchase_time</td>
<td>TIMESTAMP</td>
</tr>

<tr>
<td>amount</td>
<td>DECIMAL(10,2)</td>
</tr>
</tbody>
</table>
Output:

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

<tbody>
<tr>
<td>week_start</td>
<td>TIMESTAMP</td>
</tr>

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

<h3>Example:</h3>

Input:

<code>game_sessions</code> table

<table>
<thead>
<tr>
<th>user_id</th>
<th>session_id</th>
<th>session_start</th>
<th>country</th>
</tr>
</thead>

<tbody>
<tr>
<td>1</td>
<td>101</td>
<td>2023-10-02</td>
<td>USA</td>
</tr>

<tr>
<td>2</td>
<td>102</td>
<td>2023-10-03</td>
<td>USA</td>
</tr>

<tr>
<td>1</td>
<td>103</td>
<td>2023-10-04</td>
<td>USA</td>
</tr>

<tr>
<td>3</td>
<td>104</td>
<td>2023-10-05</td>
<td>USA</td>
</tr>
</tbody>
</table>
<code>purchases</code> table

<table>
<thead>
<tr>
<th>purchase_id</th>
<th>user_id</th>
<th>purchase_time</th>
<th>amount</th>
</tr>
</thead>

<tbody>
<tr>
<td>201</td>
<td>1</td>
<td>2023-10-02</td>
<td>5.99</td>
</tr>

<tr>
<td>202</td>
<td>2</td>
<td>2023-10-03</td>
<td>10.00</td>
</tr>

<tr>
<td>203</td>
<td>1</td>
<td>2023-10-04</td>
<td>2.99</td>
</tr>

<tr>
<td>204</td>
<td>3</td>
<td>2023-10-05</td>
<td>7.49</td>
</tr>
</tbody>
</table>
Output:

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

<tbody>
<tr>
<td>week_start</td>
<td>2023-10-02</td>
</tr>

<tr>
<td>wapu</td>
<td>3</td>
</tr>
</tbody>
</table>
Explanation:

All sessions and purchases occur during the same calendar week:

<ul>
<li>Week start: Monday 2023-10-02</li>
</ul>

Users who qualify as WAPU (≥1 session AND ≥1 purchase in the same week):

<ul>
<li>User 1 - sessions on Oct 2 &amp; 4; purchases on Oct 2 &amp; 4</li>
<li>User 2 - session on Oct 3; purchase on Oct 3</li>
<li>User 3 - session on Oct 5; purchase on Oct 5</li>
</ul>

Total WAPU = 3 users

How do you calculate weekly active paying users (WAPU) by joining gameplay and purchase activity by week?

Track Your Most Valuable Gamers

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

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

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

Note: Assume that employees only work on one project at a time.

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

Over-Budget Projects

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.

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

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

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

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

One of your favorite sports teams is playing at a local stadium, but you waited until the last minute to buy a ticket.

You can buy a scalped (second-hand) ticket for $50, which has a 20% chance of not working. If the scalped ticket doesn’t work, you’ll have to buy a box office ticket for $70 at the stadium.

<ol>
<li>How much do you expect to pay to go to the sports game?</li>
<li>How much money should you set aside for the game?</li>
</ol>

Scalped Ticket

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.

Given the integer list nums with length n, create a function that converts each integer in the list into its corresponding Roman numeral representation. The Roman numeral symbols and corresponding values are provided in the table below. The function must be able to handle integers up to 1000.

<table>
<thead>
<tr>
<th>Symbols</th>
<th>Values</th>
</tr>
</thead>

<tbody>
<tr>
<td>I</td>
<td>1</td>
</tr>

<tr>
<td>IV</td>
<td>4</td>
</tr>

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

<tr>
<td>IX</td>
<td>9</td>
</tr>

<tr>
<td>X</td>
<td>10</td>
</tr>

<tr>
<td>XL</td>
<td>40</td>
</tr>

<tr>
<td>L</td>
<td>50</td>
</tr>

<tr>
<td>XC</td>
<td>90</td>
</tr>

<tr>
<td>C</td>
<td>100</td>
</tr>

<tr>
<td>CD</td>
<td>400</td>
</tr>

<tr>
<td>D</td>
<td>500</td>
</tr>

<tr>
<td>CM</td>
<td>900</td>
</tr>

<tr>
<td>M</td>
<td>1000</td>
</tr>
</tbody>
</table>

<h3>Examples:</h3>

Input:

<pre tabindex="0" class="chroma"><code>nums = [1, 4, 179]
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>[&#34;I&#34;, &#34;IV&#34;, &#34;CLXXIX&#34;]
</code></pre>

Create a function that converts each integer in the list into its corresponding Roman numeral representation

Integer to Roman

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 we’re comparing two machine learning algorithms. In which case would you use a bagging algorithm versus a boosting algorithm? 

Give an example of the tradeoffs between the two.

In machine learning, when would you use a bagging algorithm over a boosting algorithm?

In machine learning, when would you use a bagging algorithm over a boosting algorithm?

Bagging vs. Boosting

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>

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