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Workforce Opportunity Services Interview Questions

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Write a SQL query to count transactions filtered by several criterias.

Count Transactions

Write a query to create a pivot table that shows total sales for each branch by year

Branch Sales Pivot

Display the origin, destination, departure time, aircraft model, and capacity of a flight route.

Flight Routes

Behavioral

How would you answer when an Interviewer asks why you applied to their company?

Why Do You Want to Work With Us

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

Your Strengths and Weaknesses

Machine Learning

What metrics would you use to track accuracy and validity of the model

Spam Classifier 

Addressing imbalanced data in machine learning through carefully prepared techniques.

Data Preparation for Imbalanced Data

Data Modeling

Design a schema for a deck of playing cards and write queries to determine a hand and the best five-card hand

Design Poker Schema

Data Pipelines

Design a scalable ETL pipeline for ingesting heterogeneous data from Skyscanner's partners.

<p>You work as a data engineer at Skyscanner. Your team is responsible for ingesting, transforming, and loading flight and hotel availability data from dozens of external partner APIs into Skyscanner’s real-time search platform. The system must ensure high data freshness, fault tolerance, and scalability as new partners are onboarded.</p>

<p>How would you architect the ETL pipeline to support these requirements? Design a system flow explaining this architecture.</p>


<p>You’re working as a data analyst at Delta Airlines, and you’re provided with two tables: <code>flights</code> and <code>aircrafts</code>.</p>

<p>Write a SQL query to list all flights scheduled between <strong>Mumbai (<code>&#39;BOM&#39;</code>) and Delhi (<code>&#39;DEL&#39;</code>)</strong> that use an aircraft whose capacity is greater than the average capacity of all aircrafts in the database.</p>

<h2>Schema:</h2>

<p>Input Tables:</p>

<h3><code>flights</code></h3>

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

<tbody>
<tr>
<td>flight_id</td>
<td>INTEGER</td>
</tr>

<tr>
<td>origin</td>
<td>VARCHAR</td>
</tr>

<tr>
<td>destination</td>
<td>VARCHAR</td>
</tr>

<tr>
<td>departure_time</td>
<td>DATETIME</td>
</tr>

<tr>
<td>arrival_time</td>
<td>DATETIME</td>
</tr>

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

<h3><code>aircrafts</code></h3>

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

<tbody>
<tr>
<td>aircraft_id</td>
<td>INTEGER</td>
</tr>

<tr>
<td>model</td>
<td>VARCHAR</td>
</tr>

<tr>
<td>capacity</td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
<p>Output:</p>

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

<tbody>
<tr>
<td>origin</td>
<td>VARCHAR</td>
</tr>

<tr>
<td>destination</td>
<td>VARCHAR</td>
</tr>

<tr>
<td>departure_time</td>
<td>DATETIME</td>
</tr>

<tr>
<td>aircraft_model</td>
<td>VARCHAR</td>
</tr>

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

<h2>Example:</h2>

<p>Input Tables:</p>

<h3><code>flights</code></h3>

<table>
<thead>
<tr>
<th>flight_id</th>
<th>origin</th>
<th>destination</th>
<th>departure_time</th>
<th>arrival_time</th>
<th>aircraft_id</th>
</tr>
</thead>

<tbody>
<tr>
<td>1</td>
<td>BOM</td>
<td>DEL</td>
<td>2025-11-19 08:00</td>
<td>2025-11-19 10:00</td>
<td>201</td>
</tr>

<tr>
<td>2</td>
<td>DEL</td>
<td>BLR</td>
<td>2025-11-19 11:00</td>
<td>2025-11-19 13:00</td>
<td>202</td>
</tr>
</tbody>
</table>

<h3><code>aircrafts</code></h3>

<table>
<thead>
<tr>
<th>aircraft_id</th>
<th>model</th>
<th>capacity</th>
</tr>
</thead>

<tbody>
<tr>
<td>201</td>
<td>A320</td>
<td>180</td>
</tr>

<tr>
<td>202</td>
<td>B737</td>
<td>160</td>
</tr>
</tbody>
</table>
<p>Output:</p>

<table>
<thead>
<tr>
<th>origin</th>
<th>destination</th>
<th>departure_time</th>
<th>aircraft_model</th>
<th>capacity</th>
</tr>
</thead>

<tbody>
<tr>
<td>BOM</td>
<td>DEL</td>
<td>2025-11-19 08:00</td>
<td>A320</td>
<td>180</td>
</tr>
</tbody>
</table>
<p><em>Explanation:</em></p>

<p>The flight from BOM → DEL uses aircraft 201 with capacity 180, which is greater than the average capacity of all aircrafts. Hence, it is included in the output.</p>


<p>Let’s say you are tasked with building a spam classifier for emails. Assume that you’ve built a V1 of the model.</p>

<p>What metrics would you use to track accuracy and validity of the model?</p>


<p>You are given a dictionary with two keys <code>a</code> and <code>b</code> that hold integers as their values.</p>

<p>Without declaring any other variable, swap the value of <code>a</code> with the value of <code>b</code> and vice versa.</p>

<p><em>Note: Return the dictionary after editing it.</em></p>

<p><strong>Example:</strong></p>

<p><strong>Input:</strong></p>

<pre tabindex="0" class="chroma"><code><span class="line"><span class="cl"><span class="n">numbers</span> <span class="o">=</span> <span class="p">{</span>
</span></span><span class="line"><span class="cl">  <span class="s1">&#39;a&#39;</span><span class="p">:</span><span class="mi">3</span><span class="p">,</span>
</span></span><span class="line"><span class="cl">  <span class="s1">&#39;b&#39;</span><span class="p">:</span><span class="mi">4</span>
</span></span><span class="line"><span class="cl"><span class="p">}</span>
</span></span></code></pre>

<p><strong>Output:</strong></p>

<pre tabindex="0" class="chroma"><code><span class="line"><span class="cl"><span class="k">def</span> <span class="nf">swap_values</span><span class="p">(</span><span class="n">numbers</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="p">{</span><span class="s1">&#39;a&#39;</span><span class="p">:</span><span class="mi">4</span><span class="p">,</span><span class="s1">&#39;b&#39;</span><span class="p">:</span><span class="mi">3</span><span class="p">}</span>
</span></span></code></pre>


Swap the values of `a` and `b` in the dictionary without declaring any other variable

Swap Variables

Data Structures & Algorithms

<p>How would you handle the data preparation for building a machine learning model using imbalanced data?</p>


<p>When an interviewer asks you a question along the lines of:</p>

<ul>
<li>Why did you apply to our company?</li>
<li>What are you looking for in your next job?</li>
<li>What makes you a good fit for our company?</li>
</ul>

<p>How should you respond?</p>


<p>When asked 'What are you looking for in your next job?' in an interview, how can you tie the company's employee benefits into your response?</p>

When asked 'What are you looking for in your next job?' in an interview, how can you tie the company's employee benefits into your response?

Why Do You Want to Work With Us I

<p>How can company values be used effectively in an interview when asked 'What makes you a good fit for our company?'</p>

How can company values be used effectively in an interview when asked 'What makes you a good fit for our company?'

Why Do You Want to Work With Us II

<p>When responding to the question 'Why did you apply to our company?' during an interview, what aspect should you highlight?</p>

When responding to the question 'Why did you apply to our company?' during an interview, what aspect should you highlight?

Why Do You Want to Work With Us III

<p>When an interviewer asks a question along the lines of:</p>

<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>

<p>How would you respond?</p>


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

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

Your Strengths and Weaknesses I

<p>Which of the following is an acceptable strategy when discussing weaknesses in an interview?</p>

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

Your Strengths and Weaknesses II

<p>Your company, a multinational retail corporation, has been storing sales data from various branches worldwide in separate tables according to the year the sales were made. The current data structure is proving inefficient for business analytics and the management has requested your expertise to streamline the data.</p>

<p>Write a query to create a pivot table that shows total sales for each branch by year.</p>

<p><em>Note: Assume that the sales are represented by the <code>total_sales</code> column and are in USD. Each branch is represented by its unique <code>branch_id</code></em>.</p>

<h3><strong>Example:</strong></h3>

<p><strong>Input:</strong></p>

<p>For simplicity, consider two years: 2021 and 2022.</p>

<p><code>sales_2021</code> table</p>

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

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

<tr>
<td>branch_id</td>
<td>INTEGER</td>
</tr>

<tr>
<td>total_sales</td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
<p><code>sales_2022</code> table</p>

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

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

<tr>
<td>branch_id</td>
<td>INTEGER</td>
</tr>

<tr>
<td>total_sales</td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
<p><strong>Output:</strong></p>

<p><code>sales_pivot</code> table</p>

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

<tbody>
<tr>
<td>branch_id</td>
<td>INTEGER</td>
</tr>

<tr>
<td>total_sales_2021</td>
<td>INTEGER</td>
</tr>

<tr>
<td>total_sales_2022</td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
<p>This output pivot table shows the total sales for each branch, broken down by year.</p>


<p>Design a schema representing a deck of playing cards for playing poker.</p>

<ol>
<li><p>For example, if a user is given five cards, how would you query the database to determine what cards you have?</p></li>

<li><p>How would you write a query to determine out of nine players, which user has the best five-card hand?</p></li>
</ol>


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

Workforce Opportunity Services Interview Questions

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