Prepare for and practice interview questions from Grab across topics like Algorithms, Python, SQL and more.

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Probability

What is the probability that each subsequent card is larger than the previous drawn card when picking three cards from a shuffled deck of 500 cards

500 Cards

First to Six

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

Raining in Seattle

What is the probability that item X would be found on Amazon's website?

Found Item

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

Median Probability

A/B Testing

How would you assess the validity of the result?

Experiment Validity

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

Pre-Launching Shows

Button AB Test

A/B Test Power Size

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

Network Experiment Design

Machine Learning

How would you encode a categorical variable with thousands of distinct values

Encoding Categorical Features

How would we build a model to detect fraud and text customers to approve or deny fraudulent transactions

Bank Fraud Model

How would we know if we have enough data to create an accurate enough model?

One Million Rides

In which case would you use a bagging algorithm versus a boosting algorithm

Bagging vs Boosting

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

Type-ahead Search

Product Sense & Metrics

What would you investigate to diagnose what's happening?

WAU vs Open Rates

Calculate the formula for the average lifetime value

Revenue Retention

How would you measure the success of Uber Eats?

Uber Eats Success

What would you investigate to figure out what happened

Increased Cancellations

What are the top five metrics you would start tracking to understand the health of Google Docs

Docs Metrics

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

User Experience Percentage

Distance Traveled

Write a query to get the average order value by gender rounded to two decimal places

Average Order Value

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

Download Facts

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

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

Data Structures & Algorithms

Given a string <code>n</code>, a multi-digit number, write a function <code>rearranging_digits</code> to return a string of the smallest number larger than <code>n</code> that can be created by rearranging the digits in <code>n</code>. Return <code>None</code> if no such number exists.

Example:

Input:

<pre tabindex="0" class="chroma"><code>n = &#39;395&#39;
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>def rearranging_digits(n) -&gt; &#39;539&#39;
</code></pre>

Given a string n, a multi-digit number, write a function to return a string of the smallest number larger than n that can be created by rearranging the digits in n.

Rearranging Digits

We want to determine if Uber Eats has a net positive value for the company Uber.

How would you measure the success of Uber Eats?

If Uber wants to evaluate the customer loyalty and repeat business on Uber Eats, which of the following metrics would be the most relevant?

If Uber wants to evaluate the customer loyalty and repeat business on Uber Eats, which of the following metrics would be the most relevant?

Uber Eats Success I

Which among the following metrics would be most indicative of the operational efficiency of Uber Eats?

Which among the following metrics would be most indicative of the operational efficiency of Uber Eats?

Uber Eats Success II

Which among the following metrics would be most critical in evaluating the overall profitability of Uber Eats for Uber?

Which among the following metrics would be most critical in evaluating the overall profitability of Uber Eats for Uber?

Uber Eats Success III

When an interviewer asks a question along the lines of:

<ul>
<li>What would your current manager say about you? What constructive criticisms might he give?</li>
<li>What are your three biggest strengths and weaknesses you have identified in yourself?</li>
</ul>

How would you respond?

When asked about your strengths in an interview, what is an effective way to respond?

When asked about your strengths in an interview, what is an effective way to respond?

Your Strengths and Weaknesses I

Which of the following is an acceptable strategy when discussing weaknesses in an interview?

Which of the following is an acceptable strategy when discussing weaknesses in an interview?

Your Strengths and Weaknesses II

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

Your Strengths and Weaknesses

Behavioral

Let’s say you’re analyzing an AB test that has both a test group and a control group.

<ol>
<li>How do you calculate the sample size necessary for an accurate measurement?</li>

<li>Let’s say that the sample size is similar and sufficient between the two groups. In order to measure very small differences between the two, should the power get bigger or smaller?</li>
</ol>

Let’s say that your company is running a standard control and variant AB test on a feature to increase conversion rates on the landing page. The PM checks the results and finds a .04 p-value.

How would you assess the validity of the result?

Given 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>

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

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

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

P-value to a Layman I

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

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

P-value to a Layman II

Explain what a p-value is to someone who is not technical

P-value to a Layman

Statistics

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>

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 a table called <code>user_experiences</code>, write a query to determine the percentage of users that held the title of <code>&#34;Data Analyst&#34;</code> immediately before holding the title <code>&#34;Data Scientist&#34;</code>.

Immediate is defined as the user holding no other titles between the <code>&#34;Data Analyst&#34;</code> and <code>&#34;Data Scientist&#34;</code> roles.

Example:

Input:

<code>user_experiences</code> table

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

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

<tr>
<td><code>position_name</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>start_date</code></td>
<td>DATETIME</td>
</tr>

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

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

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

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

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

Example:

Input:

<code>users</code> table

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

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

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

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

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

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

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

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

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

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

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

Given a dictionary with weights, write a function <code>random_key</code> that returns a key at random with a probability proportional to the weights.

Example 1:

Input:

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

Output:

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

Example 2:

Input:

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

Output:

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

Weighted Keys

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

Let’s say you work at Uber. You’re getting reports that riders are complaining about the Uber map showing wrong location pickup spots.

How would you go about verifying how frequently this is happening?

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

You're getting reports that riders are complaining about the Uber map showing wrong location pickup spots. How would you go about verifying how frequently this is happening?

Uber User Journey

Analytics

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

Note: Sort the results by the <code>user_id</code> in ascending order. If a user purchases two products at the same time, the lower <code>id</code> field is used to determine which is the first purchase.

Example:

Input:

<code>transactions</code> table

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

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

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

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

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

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

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

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

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

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

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

Write a query to find the third purchase of every user sorted by user_id ascending

Third Purchase

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

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

Example:

Input:

<code>download_facts</code> table

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

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

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

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

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

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

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

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

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

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

Top 3 Users

Given three tables, representing customer transactions and customer attributes:

Write a query to get the average order value by gender.

Note: We’re looking at the average order value by users that have ever placed an order. Additionally, please round your answer to two decimal places.

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>
<code>users</code> table

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

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

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

<tr>
<td><code>sex</code></td>
<td>VARCHAR</td>
</tr>
</tbody>
</table>
<code>products</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>price</code></td>
<td>FLOAT</td>
</tr>
</tbody>
</table>
Output:

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

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

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

Lime’s consumer app team wants to measure the number of users who return to the app after their initial login. Given a user_logins table that records each user’s login by date, write a query to produce a daily summary with retention and engagement metrics.

For each date that appears in the user_logins table, calculate daily retention metrics including cohort size, 7-day retention, retention rate, and rolling 7-day active users. (A cohort for a given date consists of all users who logged in on that specific date (regardless of whether it was their first login or a subsequent login).

Notes:

<ul>
<li>A user is considered retained if they log in again within the next 7 days after their cohort date (days 2-8), excluding the cohort day itself.</li>
<li>The rolling 7-day users includes all distinct users who logged in during the 7-day window ending on the current date (from login_date - 6 days to login_date, inclusive).</li>
<li>Each (user_id, login_date) pair is unique - users can only log in once per day.
The output should be ordered by login_date ascending.</li>
<li>Retention rate should be calculated as: (retained_within_7d / cohort_users) * 100, rounded to 1 decimal place.</li>
</ul>

<h3>Schema:</h3>

Input:

<code>user_logins</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>login_date</code></td>
<td>DATE</td>
</tr>
</tbody>
</table>
Output:

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

<tbody>
<tr>
<td><code>login_date</code></td>
<td>DATE</td>
</tr>

<tr>
<td><code>cohort_users</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>retained_within_7d</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>retention_rate</code></td>
<td>DECIMAL(3, 2)</td>
</tr>

<tr>
<td><code>rolling_7d_users</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>

<h3>Example:</h3>

Input:

<table>
<thead>
<tr>
<th>user_id</th>
<th>login_date</th>
</tr>
</thead>

<tbody>
<tr>
<td>1</td>
<td>2024-06-01</td>
</tr>

<tr>
<td>2</td>
<td>2024-06-01</td>
</tr>

<tr>
<td>1</td>
<td>2024-06-02</td>
</tr>

<tr>
<td>2</td>
<td>2024-06-08</td>
</tr>

<tr>
<td>3</td>
<td>2024-06-08</td>
</tr>

<tr>
<td>1</td>
<td>2024-06-09</td>
</tr>

<tr>
<td>3</td>
<td>2024-06-10</td>
</tr>
</tbody>
</table>
Output:

<table>
<thead>
<tr>
<th>login_date</th>
<th>cohort_users</th>
<th>retained_within_7d</th>
<th>retention_rate</th>
<th>rolling_7d_users</th>
</tr>
</thead>

<tbody>
<tr>
<td>2024-06-01</td>
<td>2</td>
<td>1</td>
<td>50.0</td>
<td>2</td>
</tr>

<tr>
<td>2024-06-02</td>
<td>1</td>
<td>1</td>
<td>100.0</td>
<td>2</td>
</tr>

<tr>
<td>2024-06-08</td>
<td>2</td>
<td>1</td>
<td>50.0</td>
<td>2</td>
</tr>

<tr>
<td>2024-06-09</td>
<td>1</td>
<td>0</td>
<td>0.0</td>
<td>2</td>
</tr>

<tr>
<td>2024-06-10</td>
<td>1</td>
<td>0</td>
<td>0.0</td>
<td>2</td>
</tr>
</tbody>
</table>
Explanation:

Retention is calculated for users who return within 7 days after their first login on a given day. Rolling 7-day user counts unique users over the current and previous 6 days.*

Suppose the current <code>login_date</code> is October 10, 2027.

<ul>
<li>The cohort for this date includes all users who logged in on 2027-10-10.</li>
<li>A user is considered retained if they log in again between 2027-10-11 and 2027-10-17 (inclusive).</li>
<li>The cohort day itself (2027-10-10) is excluded from this retention window.</li>
</ul>

How would you calculate daily user retention and rolling activity for Lime's app using login data?

Daily Retention Summary

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?

Write a Python function called <code>max_profit</code> that takes a list of integers, where the i-th integer represents the price of a given stock on day i, and returns the maximum profit you can achieve by buying and selling the stock.

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

Input:

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

Output:

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

Explanation:

<pre><code>Buy on day 4 (price = 0) and sell on day 6 (price = 3): profit = 3 - 0 = 3.
Then buy again on day 7 (price = 1) and sell on day 8 (price = 4): profit = 4 - 1 = 3.
Total profit: 3 + 3 = 6.
</code></pre>

Find the maximum profit from at most two stock buy/sell transactions

Maximum Profit

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

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

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

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>

Compute the probability a user gets exactly 0 impressions, and the probability everyone gets at least 1 impression

Impression Reach

Given a list of <code>integers</code>, and an integer <code>N</code>, write a function <code>sum_to_n</code> to find all combinations that sum to the value <code>N</code>.

Example: 

Input:

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

Output:

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

Given a list of integers, and an integer N, write a function to find all combinations that sum to the value N.

Sum to N

Netflix has hired people to rate movies.

Out of all of the raters, 80% of the raters carefully rate movies and rate 60% of the movies as good and 40% as bad. The other 20% are lazy raters and rate 100% of the movies as good.

Assuming all raters rate the same amount of movies, what is the probability that a movie is rated good?

There are two categories of movie raters: careful and lazy. Careful raters make up 80% of the total. They rate 60% of the movies as good and 40% as bad. Lazy raters, constitute 20% of the raters and 100% of the movies as good. Given these parameters, calculate the overall probability of a movie being rated good.

There are two categories of movie raters: careful and lazy. Careful raters make up 80% of the total. They rate 60% of the movies as good and 40% as bad. Lazy raters, constitute 20% of the raters and 100% of the movies as good. Given these parameters, calculate the overall probability of a movie being rated good.

Lazy Raters

What is the probability that a movie is rated good

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

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?

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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