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Cascade Data Labs Interview Questions

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Machine Learning

Which model would perform better and why?

Booking Regression

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

Bank Fraud Model

How does random forest generate the forest and why use it over logistic regression

Random Forest Explanation

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

Xgboost vs Random Forest

Explain how to build an AUC metric and evaluate three binary classification models using it

Area Under the ROC Curve

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

Skyscanner Partner ETL

Aggregating and collecting unstructured data.

Unstructured Data Pipeline (ETL)

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

Scalable Data Pipelines

Behavioral

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

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

Your Strengths and Weaknesses

ML Ops

Design a strategy for monitoring and evaluating a machine learning model's real-time performance after deployment.

Production Model Monitoring

How would you get a model ready for production use and adaptation to new datasets?

Model Deployment Preparation

Design an end-to-end retraining and production pipeline for a machine learning model without dedicated infrastructure or support staff.

Solo ML Deployment

Statistics

What are the assumptions of linear regression?

Assumptions of Linear Regression

How do you detect and handle correlation between variables in linear regression, and what happens if you ignore it

Correlation in Regression

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

Given two strings <code>A</code> and <code>B</code>, write a function <code>can_shift</code> to return whether or not <code>A</code> can be shifted some number of places to get <code>B</code>.

Example:

Input:

<pre tabindex="0" class="chroma"><code>A = &#39;abcde&#39;
B = &#39;cdeab&#39;
can_shift(A, B) == True

A = &#39;abc&#39;
B = &#39;acb&#39;
can_shift(A, B) == False
</code></pre>

Write a function can_shift to return whether or not A can be shifted some number of places to get B

String Shift

Data Structures & Algorithms

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

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

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

<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>

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

Largest Salary by Department

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?

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?

Let’s say we want to build a model to predict booking prices on Airbnb.

Between linear regression and random forest regression, which model would perform better and why?

What are the two key concepts of random forest regression?

What are the two key concepts of random forest regression?

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

Addressing imbalanced data in machine learning through carefully prepared techniques.

Data Preparation for Imbalanced Data

Let’s say that you work on the revenue forecasting team at a company like Facebook.

An executive comes to you asking about how much revenue Facebook will make in the coming year.

How would you forecast revenue for the next year?

How would you forecast revenue for the next year

Forecasting New Year Revenue

Forecasting & Time Series

How does random forest generate the forest? Additionally, why would we use it over other algorithms such as logistic regression?

What happens when you average the output on multiple decision trees?

What happens when you average the output on multiple decision trees?

Average Trees

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.

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

You are required to extract a specific word <code>target_word</code> from an encrypted document. Fortunately, you have the basic information needed to decrypt that document.

The equation used to encrypt the document is as follows:

\[E_n(x) = (I(\mathsf{s}) + n) \mod 26 \]

Where:

<ul>
<li>$\mathsf{s}$ is a letter</li>
<li>$I(x) $ is the number representing the position of the letter in the alphabet (1 for <code>A</code>, 2 for <code>B</code>, etc.)</li>
<li>$n $ is an arbitrary integer.</li>
<li>$E_n(x) $ is the encrypted letter.</li>
</ul>

Return the number of occurrences of <code>target_word</code> in the document after decryption.

Note: The document only has upper case letters and spaces.

Example:

Input:

<pre tabindex="0" class="chroma"><code>encrypted_document = &#39;WKH TXLFN EURZQ IRA MXPSV RYHU WKH ODCB GRJ&#39;
n = 3
target_word = &#39;BROWN&#39;
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>extract_word(encrypted_document,n,target_word) -&gt; 1
</code></pre>

Explanation:

After decrypting the document, you will have :

<pre tabindex="0" class="chroma"><code>THE QUICK BROWN FOX JUMPS OVER THE LAZY DOG
</code></pre>

In which the <code>target_word</code> (BROWN) appears only once.

Return the number of occurrences of `target_word` in the decrypted document

Words in Encrypted String

You work as a data scientist at Cars.com. The company is launching a new personalized car recommendation engine that must support both real-time and batch inference, adapt to changing user preferences, and enable frequent model updates via CI/CD pipelines.

How would you design the end-to-end architecture for this system, ensuring it can robustly detect and respond to data distribution shifts while maintaining seamless CI/CD for machine learning models?

Design an end-to-end ML system for personalized car recommendations at Cars.com, supporting real-time and batch inference, data distribution shift detection, and CI/CD for frequent model updates.

Car Recommendation Architecture

What are the assumptions of linear regression?

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

Regression assumptions

Let’s say that you’re training a classification model.

How would you combat overfitting when building tree-based models?

Let's say that you're training a classification model.   How would you combat overfitting when building tree-based models?

Overfit Avoidance

Let’s say you work as an LLM engineer at an enterprise software company. You are tasked with designing a retrieval-augmented generation (RAG) system to ensure that the language model only provides answers based on a specific set of ingested documents, and never responds using its original training data.

How would you architect this system?

Design a RAG system that restricts LLM responses exclusively to ingested data, preventing use of pre-trained knowledge.

RAG Strict Source Control

AI & Agentic Systems

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.

How would you build this data pipeline?

Let’s say you work as a machine learning engineer at a ride-hailing company. You have recently deployed a new ETA prediction model to production.

How would you monitor and evaluate the real-time performance of this model after deployment?

Let’s say we’ve built a churn prediction model for a subscription service using user behavior data from the past 12 months, and now we want to deploy it to production and also apply it to a new user segment that wasn’t in our original training data.

How would you approach preparing the model for deployment and ensuring it generalizes well to the new dataset?

You work as the sole machine learning engineer at a mid-sized e-commerce company. You have developed a predictive model for customer purchase likelihood that needs to be retrained periodically and reliably served in production. There is no dedicated data engineering or ML-Ops support.

How would you automate the retraining and deployment of this model while ensuring reliability and minimal downtime?

When an interviewer asks you a question along the lines of:

<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>

How should you respond?

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?

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

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

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

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

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

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

Let’s say you’re a data engineer at a rapidly expanding e-commerce company, and the volume of daily transactions has increased from 10,000 to 100,000 over the past year.
How would you approach designing or adapting your data pipelines to ensure they remain both scalable and reliable as the business continues to grow?

Let’s say that you’re in charge of getting payment data into your internal data warehouse.

How would you build an ETL pipeline to get Stripe payment data into the database so analysts can build revenue dashboards and run analytics?

Let’s say you work as a data scientist on the engagement team.

<ol>
<li>How would you measure the success of Linkedin’s newsfeed ranking algorithm?</li>

<li>Let’s say some of the success metrics that you are tracking for the newsfeed algorithm are going up while others are going down. How would you approach this?</li>
</ol>

How would you measure the success of Linkedin’s newsfeed ranking algorithm and approach conflicting success metrics

Ranking Metrics

Product Sense & Metrics

Let’s say we’re working with a B2B SaaS company that wants to land a contract with a Fortune 500 firm, but their decision-makers rarely respond to standard outreach or generic marketing materials.

How would you use analytics to design and measure a personalized content strategy that increases engagement with this high-value account?

How would you design and measure a personalized content approach for a hard-to-reach enterprise client?

Account Personalization Strategy

You are designing an ETL pipeline for a model that uses videos as input.

How would you collect and aggregate data for multimedia information, specifically when it’s unstructured data from videos?

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

Suppose you are given three models for a binary classification problem. Each model gives you a probability of an input belonging to class 1.

You are asked to build an area under the ROC curve metric to evaluate the model performance. Briefly explain how you will do that.

After you make the metric and make sure it works correctly, you apply it on three models and get the following results:

<ul>
<li>Model 1: 0.1</li>
<li>Model 2: 0.5</li>
<li>Model 3: 0.8</li>
</ul>

Briefly explain your evaluation of these models and choose one to use for the classifier.

Assume you are receiving CSV files intermittently utilizing an SFTP. Design a pipeline to make this data usable.

SFTP Pipeline

An ecommerce company is experiencing a reduction in revenue for the past 12 months. You have the following transaction data:

<ul>
<li>Date of sale</li>

<li>Total $ Amount paid by customer</li>

<li>Profit Margin per unit</li>

<li>Quantity of item</li>

<li>Item category</li>

<li>Item subcategory</li>

<li>Marketing attribution source</li>

<li>% discount applied</li>
</ul>

How would you analyze the dataset to understand exactly where the revenue loss is occurring?

How would you analyze the dataset to understand exactly where the revenue loss is occurring?

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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