Prepare for and practice interview questions from PulsePoint across topics like Python, SQL, Product Metrics and more.

PulsePoint Interview Questions

PulsePoint Interview Guides

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

Write a SQL query to create a histogram of the number of comments per user in the month of January 2020.

Comments Histogram

Write a query that returns all neighborhoods that have 0 users.

Empty Neighborhoods

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

First Touch Attribution

Data Structures & Algorithms

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

Merge Sorted Lists

Find Bigrams

Write a function to create a single dataframe with complete addresses in the format of street, city, state, zip code.

Complete Addresses

Write a function friendship_timeline to generate an output that lists the pairs of friends with their corresponding timestamps of the friendship beginning and then the timestamp of the friendship ending. 

Friendship Timeline

Get Top N Frequent Words

Machine Learning

Encoding Categorical Features

Coefficients of Logistic Regression

Booking Regression

Job Recommendation

RMS Error

Product Sense & Metrics

How would you investigate a decline in the average number of comments per user?

Decreasing Comments

Promoting Instagram

Threaded Comments

Group Success

WAU vs Open Rates

Probability

First to Six

500 Cards

Raining in Seattle

Found Item

Ad Raters

Given a list of timestamps in sequential order, return a list of lists grouped by week (7 days) using the first timestamp as the starting point.

Example:

Input:

<pre tabindex="0" class="chroma"><code>ts = [
 &#39;2019-01-01&#39;, 
 &#39;2019-01-02&#39;,
 &#39;2019-01-08&#39;, 
 &#39;2019-02-01&#39;, 
 &#39;2019-02-02&#39;,
 &#39;2019-02-05&#39;,
]
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>def weekly_aggregation(ts) -&gt; [
 [&#39;2019-01-01&#39;, &#39;2019-01-02&#39;], 
 [&#39;2019-01-08&#39;], 
 [&#39;2019-02-01&#39;, &#39;2019-02-02&#39;],
 [&#39;2019-02-05&#39;],
]
</code></pre>

Weekly Aggregation

Let’s say you work for a social media company that has just done a launch in a new city. Looking at weekly metrics, you see a slow decrease in the average number of comments per user from January to March in this city.

The company has been consistently growing new users in the city from January to March.

What are some reasons why the average number of comments per user would be decreasing and what metrics would you look into?

What metric could be used to measure if active users are still engaging on the platform despite an overall decrease in the average number of comments per user?

What metric could be used to measure if active users are still engaging on the platform despite an overall decrease in the average number of comments per user?

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>

Imagine a deck of 500 cards numbered from 1 to 500. If all the cards are shuffled randomly and you are asked to pick three cards, one at a time, what’s the probability of each subsequent card being larger than the previous drawn card?

Imagine a deck of 2000 cards numbered from 1 to 2000. If all the cards are shuffled randomly and you are asked to pick three cards, one at a time, what’s the probability of each subsequent card being larger than the previously drawn card?

Imagine a deck of 2000 cards numbered from 1 to 2000. If all the cards are shuffled randomly and you are asked to pick three cards, one at a time, what's the probability of each subsequent card being larger than the previously drawn card?

2000 cards

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>

Write a function called <code>find_bigrams</code> that takes a sentence or paragraph of strings and returns a list of all its bigrams in order.

Note: A bigram is a pair of consecutive words.

Example:

Input:

<pre tabindex="0" class="chroma"><code>sentence = &#34;&#34;&#34;
Have free hours and love children? 
Drive kids to school, soccer practice 
and other activities.
&#34;&#34;&#34;
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>def find_bigrams(sentence) -&gt;

 [(&#39;have&#39;, &#39;free&#39;),
 (&#39;free&#39;, &#39;hours&#39;),
 (&#39;hours&#39;, &#39;and&#39;),
 (&#39;and&#39;, &#39;love&#39;),
 (&#39;love&#39;, &#39;children?&#39;),
 (&#39;children?&#39;, &#39;drive&#39;),
 (&#39;drive&#39;, &#39;kids&#39;),
 (&#39;kids&#39;, &#39;to&#39;),
 (&#39;to&#39;, &#39;school,&#39;),
 (&#39;school,&#39;, &#39;soccer&#39;),
 (&#39;soccer&#39;, &#39;practice&#39;),
 (&#39;practice&#39;, &#39;and&#39;),
 (&#39;and&#39;, &#39;other&#39;),
 (&#39;other&#39;, &#39;activities.&#39;)]
</code></pre>

We’re given a table of product purchases. Each row in the table represents an individual user product purchase.

Write a query to get the number of customers that were upsold, or in other words, the number of users who bought additional products after their first purchase.

Note: If the customer purchased two things on the same day, that does not count as an upsell, as they were purchased within a similar timeframe.

Example:

Input:

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

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

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

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

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

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

Write a query to get the number of customers that were upsold

Upsell Transactions

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?

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

There are four people on the ground floor of a building that has five levels not including the ground floor. They all get into the same elevator.

If each person is equally likely to get on any floor and they leave independently of each other, what is the probability that no two passengers will get off at the same floor?

There are four people on the ground floor of a building that has five levels, not including the ground floor. They all get into the same elevator.

If each person is equally likely to get on any floor and they leave independently of each other, what is the probability that no two passengers will get off on the same floor?

There are four people on the ground floor of a building that has five levels, not including the ground floor. They all get into the same elevator.

If each person is equally likely to get on any floor and they leave independently of each other, what is the probability that no two passengers will get off on the same floor?

Four Person Elevator

You are about to get on a plane to Seattle. You want to know if you should bring an umbrella. You call 3 random friends of yours who live there and ask each independently if it’s raining. Each of your friends has a 2⁄3 chance of telling you the truth and a 1⁄3 chance of messing with you by lying. All 3 friends tell you that “Yes” it is raining.

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

What is the main difference between the Frequentist and Bayesian methods of probabilistic analysis?

What is the **main** difference between the Frequentist and Bayesian methods of probabilistic analysis?

Frequentist Vs Bayesian Analysis

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

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

How would we build this model?

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

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

Which statement is true?

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

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

Which statement is true?

Bank Fraud Model

In data science, there exists the concept of stemming, which is the heuristic of chopping off the end of a word to clean and bucket it into an easier feature set. 

Given a dictionary consisting of many roots and a sentence, write a function <code>replace_words</code> to stem all the words in the sentence with the root forming it. If a word has many roots that can form it, replace it with the root with the shortest length.

Example:

Input:

<pre tabindex="0" class="chroma"><code>
roots = [&#34;cat&#34;, &#34;bat&#34;, &#34;rat&#34;]
sentence = &#34;the cattle was rattled by the battery&#34;
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code> &#34;the cat was rat by the bat&#34;
</code></pre>

Given a dictionary consisting of many roots and a sentence, write a function to stem all the words in the sentence with the root forming it.

Replace Words with Stems

We’re given two tables, a <code>users</code> table with demographic information and the neighborhood they live in and a <code>neighborhoods</code> table.

Write a query that returns all neighborhoods that have 0 users. 

Example:

Input:

<code>users</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>

<tr>
<td><code>neighborhood_id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>created_at</code></td>
<td>DATETIME</td>
</tr>
</tbody>
</table>
<code>neighborhoods</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>

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

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

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

Let’s say you have to analyze the results of an AB test.

One variant of the AB test has a sample size of 50K users and the other has a sample size of 200K users.

Given the unbalanced size between the two groups, can you determine if the test will result in bias towards the smaller group?

Given an AB test where one variant has a sample size of 50K users and the other has a sample size of 200K users, what is the likely effect of the unbalanced sizes on the test results?

Given an AB test where one variant has a sample size of 50K users and the other has a sample size of 200K users, what is the likely effect of the unbalanced sizes on the test results?

Sample Size Bias

Let’s say your manager is running A/B tests on a website. You find out that your manager has run an A/B test with 20 different variants and come back to you that one of the variants is significant.

Would you think there was anything fishy about the results?

Your manager reconfigured an A/B test from 2 to 20 different variants, while keeping all other parameters unchanged. They come back to you that one of the variants is significant.

Is this conclusion likely to be correct? Why or why not?

Your manager reconfigured an A/B test from 2 to 20 different variants, while keeping all other parameters unchanged. They come back to you that one of the variants is significant.

Is this conclusion likely to be correct? Why or why not?

Twenty Variant Test

Twenty 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

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

One Element Removed

Given two tables: <code>accounts</code>, and <code>downloads</code>, find the average number of downloads for free vs paying accounts, broken down by day.

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

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

Example:

Input:

<code>accounts</code> table

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

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

<tr>
<td><code>paying_customer</code></td>
<td>BOOLEAN</td>
</tr>
</tbody>
</table>
<code>downloads</code> table

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

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

<tr>
<td><code>download_date</code></td>
<td>DATETIME</td>
</tr>

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

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

<tbody>
<tr>
<td><code>download_date</code></td>
<td>DATETIME</td>
</tr>

<tr>
<td><code>paying_customer</code></td>
<td>BOOLEAN</td>
</tr>

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

Download Facts

<ol>
<li>Given an example paragraph string and an integer <code>N</code>, write a function <code>n_frequent_words</code> that returns the top <code>N</code> frequent words in the posting and the frequencies for each word.</li>

<li>What’s the function run-time?</li>
</ol>

Example:

Input:

<pre tabindex="0" class="chroma"><code>posting = &#34;&#34;&#34;
Herbal sauna uses the healing properties of herbs in combination with distilled water. 
The water evaporates and distributes the effect of the herbs throughout the room. 
A visit to the herbal sauna can cause real miracles, especially for colds. 
&#34;&#34;&#34; 
n = 3
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>n_frequent_words(posting,N) = [
 (&#39;the&#39;, 6), 
 (&#39;herbal&#39;, 2), 
 (&#39;sauna&#39;, 2),
]
</code></pre>

Given a list of stop words, write a function <code>stopwords_stripped</code> that takes a string and returns a string stripped of the stop words with all lower case characters.

Example:

Input:

<pre tabindex="0" class="chroma"><code>stopwords = [
 &#39;I&#39;, 
 &#39;as&#39;, 
 &#39;to&#39;, 
 &#39;you&#39;, 
 &#39;your&#39;, 
 &#39;but&#39;, 
 &#39;be&#39;, 
 &#39;a&#39;,
]

paragraph = &#39;I want to figure out how I can be a better data scientist&#39;
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>def stopwords_stripped(paragraph,stopwords)
 return(stripped_paragraph) -&gt;

stripped_paragraph = &#39;want figure out how can better data scientist.&#39;
</code></pre>

Stop Words Filter

Given a table of students and their SAT test scores, write a query to return the two students with the closest test scores with the score difference.

If there are multiple students with the same minimum score difference, select the student name combination that is higher in the alphabet. 

Example:

Input:

<code>scores</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>student</code></td>
<td>VARCHAR</td>
</tr>

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

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

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

<tr>
<td><code>other_student</code></td>
<td>VARCHAR</td>
</tr>

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

Closest SAT Scores

Given an unfair coin with the probability of heads and tails not being equal, can you make an algorithm that could generate a list of uniformly distributed zeros and ones only using the results of the coin tosses?

Biased Random Number Generator

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?

Median Probability

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

A jar holds 1000 coins. Out of all of the coins, 999 are fair and one is double-sided with two heads. Picking a coin at random, you toss the coin ten times.

Given that you see 10 heads, what is the probability that the coin is double headed and the probability that the next toss of the coin is also a head?

Jars and Coins

Let’s say we’re given a biased coin that comes up heads 30% of the time when tossed.

What is the probability of the coin landing as heads exactly 5 times out of 6 tosses?

Biased five out of six

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

Button AB Test

Let’s say you’re a product data scientist at Facebook. Facebook is rolling out a new feature called “Mentions” which is an app specifically for celebrities on Facebook to connect with their fans.

How would you measure the health of the Mentions app? And if a celebrity starts using Mentions and begins interacting with their fans more, what part of the increase can be attributed to a celebrity using Mentions versus what part is just a celebrity wanting to get more involved in fan engagement?

Celebrity Mentions

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>

Rolling Bank Transactions

Let’s say you’re working with survey data sent in the form of multiple choice questions.

How would you test if survey responses were filled at random by certain individuals, as opposed to truthful selections?

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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