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Apple Leisure Group Interview Questions

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Machine Learning

Missing Housing Data

Food Prep Features

Regularization and Validation

Xgboost vs Random Forest

Analytics

Describing a data project and its challenges

Hurdles In Data Projects

Strategically resolving misaligned expectations with stakeholders for a successful project outcome

Stakeholder Communication

Data Pipelines

Design a system to synchronize two continuously updated, schema-different hotel inventory databases at Agoda.

Cross-Region Inventory Sync

Describing a real-world data cleaning and organization project

Data Cleaning Experiences

A/B Testing

Button AB Test

Comparing Search Engines

Data Structures & Algorithms

Write a function to find the best days to buy and sell a stock and the profit you generate from the sale.

Buy or Sell

<ul>
<li>At Apple Leisure Group (ALG), we care for people so they can be their best. As North America's only vertically integrated travel, hospitality, and leisure management group, ALG delivers distinctive experiences and unparalleled service to our guests, making us the first choice in vacation travel. Our portfolio of 14 leading brands includes Apple Vacations, the #1 vacation brand for packages to Mexico and the Dominican Republic.</li>
</ul>
ALG provides comprehensive travel services, from booking flights and hotels to managing luxury resorts and offering exclusive loyalty programs. We pride ourselves on our values of empathy, integrity, respect, and inclusion, creating a diverse and welcoming workplace.
Join ALG and grow your career while enriching lives and uniting the world. Dive in to learn more about our interview process and what it takes to be part of our innovative team.
Ready to embark on an exciting journey with ALG? Let's get started!

- At Apple Leisure Group (ALG), we care for people so they can be their best. As North America's only vertically integrated travel, hospitality, and leisure management group, ALG delivers distinctive experiences and unparalleled service to our guests, making us the first choice in vacation travel. Our portfolio of 14 leading brands includes Apple Vacations, the #1 vacation brand for packages to Mexico and the Dominican Republic.

ALG provides comprehensive travel services, from booking flights and hotels to managing luxury resorts and offering exclusive loyalty programs. We pride ourselves on our values of empathy, integrity, respect, and inclusion, creating a diverse and welcoming workplace.

Join ALG and grow your career while enriching lives and uniting the world. Dive in to learn more about our interview process and what it takes to be part of our innovative team.

Ready to embark on an exciting journey with ALG? Let's get started!

Overview

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

Brainteasers

Describe a data project you worked on. What were some of the challenges you faced?

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

Statistics

Talk about a time when you had trouble communicating with stakeholders. How were you able to overcome it?

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>

Select the 2nd highest salary in the engineering department

2nd Highest Salary

Explain the difference between the XGBoost and random forest algorithms and give an example where you would use one over the other.

Why might a data scientist choose to use XGBoost instead of Random Forest for a particular machine learning task?

Why might a data scientist choose to use XGBoost instead of Random Forest for a particular machine learning task?

XGBoost vs Random Forest: Choice

Tell me about a project in which you had to clean and organize a large dataset.

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

Regularization and cross-validation are two common techniques used to improve the performance of machine learning algorithms.

When should you use one versus the other?

Given the choices below, which one is an example of a scenario where we should be using regularization?

Given the choices below, which one is an example of a scenario where we should be using regularization?

Regularization Example

We want to build a model to predict housing prices in the city of Seattle. We’ve scraped 100K sold listings over the past three years but found that around 20% of the listings are missing square footage data.

How do we deal with the missing data to construct our model?

We want to build a model to predict housing prices in the city of Seattle. We’ve scraped 100K sold listings over the past three years, but have discovered that around 20% of the listings are missing square footage data.

How would you approach dealing with this missing data, in order to construct the most useful predictive model possible?

We want to build a model to predict housing prices in the city of Seattle. We’ve scraped 100K sold listings over the past three years, but have discovered that around 20% of the listings are missing square footage data.

How would you approach dealing with this missing data, in order to construct the most useful predictive model possible?



Let’s say you’re working on a building a better search engine for Google. You build it and want to see if it serves better results than the existing one in production.

How would you determine which search engine performed better? Which metrics would you track?

Let’s say we want to build a model to predict the time spent for a restaurant to prepare food from the moment an order comes in until the order is ready. 

What kind of model would we build and what features would we use?

Let’s say you’re working on DoorDash demand side deliveries. DoorDash is launching delivery services in New York City and Charlotte and needs a process for selecting dashers (delivery drivers).

Let’s say we want to create a model to determine which Dashers should deliver a particular order.

We currently have these features already in our model.

<ul>
<li>Dasher distance to the restaurant</li>
<li>Number of Dasher reviews from customers</li>
<li>Number of Dasher successful deliveries</li>
</ul>

Which feature would be the best additional input to our existing features to improve the model?

Let's say you're working on DoorDash demand side deliveries. DoorDash is launching delivery services in New York City and Charlotte and needs a process for selecting dashers (delivery drivers).

Let's say we want to create a model to determine which Dashers should deliver a particular order.

We currently have these features already in our model. 

- Dasher distance to the restaurant
- Number of Dasher reviews from customers
- Number of Dasher successful deliveries

Which feature would be the best additional input to our existing features to improve the model?



Dasher Model

Given a list of <code>stock_prices</code> in ascending order by <code>datetime</code>, and their respective dates in list <code>dts</code>, write a function <code>max_profit</code> that outputs the max profit by buying and selling at a specific interval and start and end dates to buy and sell for max profit.

Example:

Input:

<pre tabindex="0" class="chroma"><code>stock_prices = [10,5,20,32,25,12]
dts = [
 &#39;2019-01-01&#39;, 
 &#39;2019-01-02&#39;,
 &#39;2019-01-03&#39;,
 &#39;2019-01-04&#39;,
 &#39;2019-01-05&#39;,
 &#39;2019-01-06&#39;,
]

def max_profit(stock_prices,dts) -&gt; 27
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>def max_profit(stock_prices, dts) -&gt; 
(27, &#39;2019-01-02&#39;, &#39;2019-01-04&#39;)
</code></pre>

You have a function that outputs a random integer between a minimum value, \(N\), and a maximum value, \(M\). Call this function \(X\).

Take the output from the random integer function and place it into another random function as the max value with the same min value \(N\). This second function you can call \(Y\).

<ol>
<li>What would the distribution of the samples look like?</li>

<li>What would be the expected value?</li>
</ol>

Given a function that outputs a random integer between a min value N and a max value M, and then uses the output as the max value for a second function with the same min N, what is the expected value?

Given a function that outputs a random integer between a min value N and a max value M, and then uses the output as the max value for a second function with the same min N, what is the expected value?

Random Seed Function I

What does the Law of Iterated Expectation state for two random variables X and Y?

What does the Law of Iterated Expectation state for two random variables X and Y?

Random Seed Function II

Random Seed Function

Probability

You work as a data engineer at Agoda, a global travel booking platform. Agoda maintains two independent hotel inventory databases one for Asia-Pacific and one for Europe, each with different schemas but storing similar hotel and room availability data. Both databases are updated continuously by regional services.

How would you design a system to continuously synchronize hotel and room availability data between these two databases, ensuring consistency in the presence of ongoing updates?

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

Apple Leisure Group Interview Questions

Apple Leisure Group Interview Guides

Overview

Apple Leisure Group Interview Questions

Challenge