Prepare for and practice interview questions from Allstem Connections.

Allstem Connections Interview Questions

Allstem Connections Interview Guides

Machine Learning

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

Encoding Categorical Features

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

Missing Housing Data

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

Bank Fraud Model

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

Bagging vs Boosting

What are some machine learning methods for building this system

FAQ Matching

Select the 2nd highest salary in the engineering department

2nd Highest Salary

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

Rolling Bank Transactions

Get the top 3 highest employee salaries by department

Top Three Salaries

Write a query to get the largest salary of any employee by department

Largest Salary by Department

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

Count Transactions

Data Pipelines

Design a data pipeline to compute hourly, daily, and weekly active user metrics from a data lake for an hourly-refreshing dashboard

Data Pipelines and Aggregation

How would you build an ETL pipeline to ingest Stripe payment data into a warehouse for revenue dashboards and analytics

Payment Data Pipeline

Design a data pipeline that supports resumable loading for a fact table after partial failure.

Resumable Fact Table Load

How would you build a reusable data cleaning pipeline for messy user event data?

User Event Data Pipeline

How would you design data pipelines to handle rapid growth in data volume while maintaining reliability?

Scalable Data Pipelines

Behavioral

Strategically resolving misaligned expectations with stakeholders for a successful project outcome

Stakeholder Communication

Describing a data project and its challenges

Hurdles In Data Projects

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

Why Do You Want to Work With Us

How would you negotiate and resolve disagreements when a client rejects your proposed solution?

Client Solution Pushback

Making data-driven insights actionable for those without technical expertise

Simple Explanations

Product Sense & Metrics

Google Maps Improvement

How would you identify supply and demand mismatch in a ride sharing market place?

Demand Metrics

Design a dashboard that provides personalized insights, sales forecasts, and inventory recommendations for shop owners based on their transaction history, seasonal trends, and customer behavior.

Merchant Dashboard Design

How would you structure the market analysis and go-to-market plan for a new smart home security device?

Smart Home Security Launch

<h3>AllSTEM Connections Overview</h3>
<p>Welcome to the exciting world of AllSTEM Connections, where innovation meets opportunity! We specialize in connecting talent with some of the most dynamic and rewarding roles in the tech industry. From Data Architects and Embedded Software Developers to Business Analysts and Machine Learning Engineers, we cater to a broad spectrum of tech professionals.</p>
<p>At AllSTEM Connections, our mission is to empower companies and candidates alike by fostering a collaborative environment that drives data-driven decision making, innovative solutions, and strategic growth. Join us and be part of a visionary team that's shaping the future.</p>
<p>In this guide, we delve into AllSTEM Connections’ interview process and typical questions you might encounter. Ready to take the next big step in your career? Let’s get started!</p>

### AllSTEM Connections Overview

Welcome to the exciting world of AllSTEM Connections, where innovation meets opportunity! We specialize in connecting talent with some of the most dynamic and rewarding roles in the tech industry. From Data Architects and Embedded Software Developers to Business Analysts and Machine Learning Engineers, we cater to a broad spectrum of tech professionals.

At AllSTEM Connections, our mission is to empower companies and candidates alike by fostering a collaborative environment that drives data-driven decision making, innovative solutions, and strategic growth. Join us and be part of a visionary team that's shaping the future.

In this guide, we delve into AllSTEM Connections’ interview process and typical questions you might encounter. Ready to take the next big step in your career? Let’s get started!

Overview

<p>Write a SQL query to select the 2nd highest salary in the engineering department.</p>

<p><strong>Note:</strong> If more than one person shares the highest salary, the query should select the next highest salary.</p>

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

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

<p><code>employees</code> table</p>

<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>
<p><code>departments</code> table</p>

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

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

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


<p>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.</p>

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

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

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

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

<p><code>bank_transactions</code> table</p>

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

<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>


<p>Given the <code>employees</code> and <code>departments</code> table, write a query to get the top 3 highest employee salaries by department. If the department contains less that 3 employees, the top 2 or the top 1 highest salaries should be listed (assume that each department has at least 1 employee). </p>

<p><em>Note: The output should include the full name of the employee in one column, the department name, and the salary. The output should be sorted by department name in ascending order and salary in descending order.</em> </p>

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

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

<p><code>employees</code> table</p>

<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>
<p><code>departments</code> table</p>

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

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

<tbody>
<tr>
<td><code>employee_nam</code>e</td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>department_name</code></td>
<td>VARCHAR</td>
</tr>

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


<p>Given two sorted lists, write a function to merge them into one sorted list.</p>

<p><em>Bonus: What’s the time complexity?</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">list1</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">5</span><span class="p">]</span>
</span></span><span class="line"><span class="cl"><span class="n">list2</span> <span class="o">=</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">6</span><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">merge_list</span><span class="p">(</span><span class="n">list1</span><span class="p">,</span><span class="n">list2</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">5</span><span class="p">,</span><span class="mi">6</span><span class="p">]</span>
</span></span></code></pre>


<p>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?</p>


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

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

Merge Sorted Lists

Data Structures & Algorithms

<p>Let’s say you’re working in HR at a major tech company, and they ask you to find employees with a high probability of leaving the company. They tell you these employees perform well but have the lowest pay.</p>

<p>Given two tables, <code>employees</code>  and <code>projects</code>, find the five lowest-paid employees who have completed at least three projects.</p>

<p>Note: We consider projects to be completed when they have an end date, which is the same as saying their <code>End_dt</code> is not <code>NULL</code>.</p>

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

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

<p><code>employees</code> table</p>

<table>
<thead>
<tr>
<th>id</th>
<th>salary</th>
</tr>
</thead>

<tbody>
<tr>
<td>INTEGER</td>
<td>FLOAT</td>
</tr>
</tbody>
</table>
<p><code>projects</code> table</p>

<table>
<thead>
<tr>
<th>employee_id</th>
<th>project_id</th>
<th>Start_dt</th>
<th>End_dt</th>
</tr>
</thead>

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

<table>
<thead>
<tr>
<th>employee_id</th>
</tr>
</thead>

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


Find the five employees with the hightest probability of leaving the company

Top 5 Turnover Risk

<p>Given a table called <code>employees</code>, get the largest salary of any employee by department</p>

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

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

<p><code>employees</code> table</p>

<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>department</code></td>
<td>VARCHAR</td>
</tr>

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

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

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

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


<p>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 <sup>2</sup>⁄<sub>3</sub> chance of telling you the truth and a <sup>1</sup>⁄<sub>3</sub> chance of messing with you by lying. All 3 friends tell you that “Yes” it is raining.</p>

<p>What is the probability that it’s actually raining in Seattle?</p>


<p>What is the <strong>main</strong> difference between the Frequentist and Bayesian methods of probabilistic analysis?</p>


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

Frequentist Vs Bayesian Analysis

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

Raining in Seattle

Probability

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

<p>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.</p>

<p>How would we build this model?</p>


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

<p>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.</p>

<p>Which statement is true?</p>


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?

<p>Let’s say you have a categorical variable with thousands of distinct values, how would you encode it?</p>


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

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

<p>Say you are tasked with designing a data mart or data warehouse for a new online retailer.</p>

<p>How would you design the system?</p>

<p><em>Note: Sketch a star schema to explain your design.</em></p>


Design a data warehouse for a new online retailer

Retailer Data Warehouse

Data Modeling

<p>Let’s say we’re comparing two machine learning algorithms. In which case would you use a bagging algorithm versus a boosting algorithm? </p>

<p>Give an example of the tradeoffs between the two.</p>


<p>In machine learning, when would you use a bagging algorithm over a boosting algorithm?</p>


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

Bagging vs. Boosting

<p>Let’s say that you’re the PM on Google Maps.</p>

<ol>
<li><p>How would you improve Google Maps?</p></li>

<li><p>What metrics would you check to see if your feature improvements are successful?</p></li>
</ol>


<p>Given a table of employees, write a query to identify all managers.</p>

<p>A manager is defined as any employee who directly supervises at least one other employee.</p>

<p><strong>Manager Definition:</strong></p>

<ul>
<li>An employee is considered a manager if at least one other employee has them listed as their supervisor.</li>
<li>Each employee has a unique ID, and supervisors are referenced by their employee ID.</li>
</ul>

<h2>Schema:</h2>

<h3><strong>Input:</strong></h3>

<p>Table <code>employees</code></p>

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

<tbody>
<tr>
<td>employee_id</td>
<td>INTEGER</td>
</tr>

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

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

<h3><strong>Output:</strong></h3>

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

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

<h2><strong>Example:</strong></h2>

<h3><strong>Input:</strong></h3>

<p><strong><code>employees</code> table</strong></p>

<table>
<thead>
<tr>
<th><strong>employee_id</strong></th>
<th><strong>name</strong></th>
<th><strong>supervisor_id</strong></th>
</tr>
</thead>

<tbody>
<tr>
<td>1</td>
<td>Alice</td>
<td>NULL</td>
</tr>

<tr>
<td>2</td>
<td>Bob</td>
<td>1</td>
</tr>

<tr>
<td>3</td>
<td>Carol</td>
<td>1</td>
</tr>

<tr>
<td>4</td>
<td>David</td>
<td>2</td>
</tr>

<tr>
<td>5</td>
<td>Eve</td>
<td>NULL</td>
</tr>
</tbody>
</table>

<h3><strong>Output:</strong></h3>

<table>
<thead>
<tr>
<th>name</th>
</tr>
</thead>

<tbody>
<tr>
<td>Alice</td>
</tr>

<tr>
<td>Bob</td>
</tr>
</tbody>
</table>

<h3><strong>Explanation:</strong></h3>

<ul>
<li><strong>Alice</strong> is a manager because both <strong>Bob</strong> and <strong>Carol</strong> report directly to her (<code>supervisor_id = 1</code>).</li>
<li><strong>Bob</strong> is a manager because <strong>David</strong> reports directly to him (<code>supervisor_id = 2</code>).</li>
<li><strong>Eve</strong> is <strong>not</strong> a manager because no employees report to her.</li>
</ul>


Write a query to identify all employees who directly supervise at least one other employee

Identify Managers

<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>


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

Flight Routes

<p>You work as a data engineer for a retail analytics company. You are tasked with designing a data pipeline that loads transactional data into a fact table. The requirement is that if the loading process fails partway through (for example, after successfully loading 100 out of 250 records), a rerun should resume loading from the point of failure, ensuring no duplicate records are loaded and none are missing.</p>

<p>How would you architect this loading process?</p>


<p>Let’s say we have an <code>annual_payments</code> table. Answer the following three questions via SQL queries and output them as a table with the answers to each question.</p>

<ol>
<li>How many total transactions are in this table?</li>
<li>How many different users made transactions?</li>
<li>How many transactions listed as <code>&#34;paid&#34;</code> have an amount greater or equal to 100?</li>
<li>Which product made the highest revenue? (use only transactions with a <code>&#34;paid&#34;</code> status)</li>
</ol>

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

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

<p><code>annual_payments</code> table</p>

<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>amount</code></td>
<td>FLOAT</td>
</tr>

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

<tr>
<td><code>status</code></td>
<td>VARCHAR</td>
</tr>

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

<tr>
<td><code>amount_refunded</code></td>
<td>FLOAT</td>
</tr>

<tr>
<td><code>product</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>last_updated</code></td>
<td>DATETIME</td>
</tr>
</tbody>
</table>
<p><strong>Output:</strong></p>

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

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

<tr>
<td><code>answer</code></td>
<td>TEXT</td>
</tr>
</tbody>
</table>


<p>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.</p>

<p>How do we deal with the missing data to construct our model?</p>


<p>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.</p>

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


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?



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


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


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

XGBoost vs Random Forest: Choice

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

Xgboost vs Random Forest

<p>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?</p>


<p>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.</p>

<p><em>Note: Can you do it on \(O(n)\) time?</em></p>

<p><em>Note: Even though there could be many solutions, only one needs to be returned.</em></p>

<p><strong>Example 1:</strong></p>

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

<pre tabindex="0" class="chroma"><code><span class="line"><span class="cl"><span class="n">array</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span> <span class="mi">2</span> <span class="mi">3</span> <span class="mi">4</span><span class="p">]</span> 
</span></span><span class="line"><span class="cl"><span class="n">target</span> <span class="o">=</span> <span class="mi">5</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">sum_pair_indices</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">target</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="p">[</span><span class="mi">0</span> <span class="mi">3</span><span class="p">]</span> <span class="ow">or</span> <span class="p">[</span><span class="mi">1</span> <span class="mi">2</span><span class="p">]</span>
</span></span></code></pre>

<p><strong>Example 2:</strong></p>

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

<pre tabindex="0" class="chroma"><code><span class="line"><span class="cl"><span class="n">array</span> <span class="o">=</span> <span class="p">[</span><span class="mi">3</span><span class="p">]</span>
</span></span><span class="line"><span class="cl"><span class="n">target</span> <span class="o">=</span> <span class="mi">6</span> 
</span></span></code></pre>

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

<pre tabindex="0" class="chroma"><code><span class="line"><span class="cl"><span class="n">Do</span> <span class="n">NOT</span> <span class="k">return</span> <span class="p">[</span><span class="mi">0</span> <span class="mi">0</span><span class="p">]</span> <span class="k">as</span> <span class="n">you</span> <span class="n">can</span><span class="s1">&#39;t use an index twice.</span>
</span></span></code></pre>


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

<p>You work as a data engineer in X (formerly Twitter), in a team responsible for building the platform’s trending chart for monitoring global social media trends. The system ingests a high-volume stream of tweets, and your team needs to partition incoming tweets into buckets based on hashtags as they arrive.</p>

<p>How would you architect the data pipeline and select data structures to efficiently partition and store tweets by hashtag in real time?</p>


System design for real-time tweet partitioning by hashtag at Apple.

Real-Time Hashtag Partitioning

Concurrent & Distributed Systems

<p>What are the assumptions of linear regression?</p>


Which assumption of the residuals of the standard linear regression model can not be overcome by increasing the sample size?

Regression assumptions

What are the assumptions of linear regression?

Assumptions of Linear Regression

Statistics

<p>Let’s say that we want to build a chatbot system for frequently asked questions. Whenever a user writes a question, we want to return the closest answer from a list of FAQs.</p>

<p>What are some machine learning methods for building this system?</p>


<p>Let’s say you are in charge of shipments at Amazon.</p>

<p>There are two types of parcels you can ship packages in, say <em>A</em> and <em>B</em>. Packages delivered in parcel <em>A</em> are damaged during shipment with probability <em>p</em>, while packages delivered in parcel <em>B</em> are damaged with probability <em>q</em>.</p>

<p>What statistical test could you use to determine which parcel is better to use? What would the statistical test conclude if <em>p=0.4</em> and <em>q=0.6</em>?</p>

<p><em>Note:</em> Assume the values of <em>p</em> and <em>q</em> were obtained from data recorded from 200 shipments, half delivered with parcel <em>A</em> and half delivered with parcel <em>B</em>.</p>


What statistical test could you use to determine which of two parcel types is better to use, given how often they are damaged?

Different Parcel Effectiveness

<p>Assume that you have data on student test scores, and they’re in one of the following layouts (dataset 1 and dataset 2).</p>

<p>What are the drawbacks of having the data organized in such a way? What formatting changes would you make so the data is more useful for analysis?</p>

<p>Additionally, describe some of the common problems seen in “messy” datasets.</p>

<p><strong>Dataset 1 and 2:</strong></p>

<p><img src="/s3/questions/a69a2739-0c3e-4841-b1a0-83f8c07c35e1.png" alt="Messy Dataset"/></p>


Challenges of specific student test score layouts, recommended formatting changes for enhanced analysis, and common issues found in "messy" datasets.

Digitizing Student Test Scores

Analytics

<p>What are the key differences between classification models and regression models?</p>


<p>What is the MOST important difference between regression and classification models?</p>


What is the MOST important difference between regression and classification models?

Classification vs Regression

Explain the key differences between classification models and regression models

Classification and Regression

<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

<p>Let’s say you work at Shopify. Your team is building a dashboard for merchants that provides personalized insights on sales trends, inventory status (e.g., overstocked or low-stock items), and customer behavior (e.g., new vs. returning customers) based on transaction history and seasonal trends. The goal is to help shop owners make better day-to-day decisions through clear and actionable data.</p>

<p>What key metrics and visualizations would you include in the dashboard, and how would you ensure they remain useful across different types of merchants?</p>


<p>Let’s say you’re a data engineer at Fidelity Investments, and you’re running a SQL query on a cloud-based data warehouse. All cluster resources and network health metrics look normal, but the query is still taking over 10 minutes to complete.</p>

<p>How would you go about diagnosing and improving the performance of this query?</p>


How would you diagnose and speed up a slow SQL query when system metrics look healthy?

Slow SQL Query

Query Optimization

<p>Let’s say you have analytics data stored in a data lake. An analyst tells you they need hourly, daily, and weekly active user data for a dashboard that refreshes every hour.</p>

<p>How would you build this data pipeline?</p>


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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