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The University Of British Columbia Interview Questions

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Machine Learning

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

Encoding Categorical Features

Interpret coefficients of logistic regression for categorical and boolean variables

Coefficients of Logistic Regression

Explain the key differences between classification models and regression models

Classification and Regression

Bias variance tradeoff and class imbalance in finance

Bias - Variance Tradeoff and Class Imbalance in Finance

Evaluate News

Statistics

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

P-value to a Layman

What are the assumptions of linear regression?

Assumptions of Linear Regression

What is the downside of only using the R-Squared (R^2) value to determine a relationship between two variables

Using R Squared

How would you tackle multicollinearity in multiple linear regression

Multicollinearity in Regression

Determine whether using an 83% cutoff overestimates or underestimates the actual population’s credit score

Credit Score Estimation

Behavioral

Describing a data project and its challenges

Hurdles In Data Projects

Strategically resolving misaligned expectations with stakeholders for a successful project outcome

Stakeholder Communication

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

How would you investigate and address a decline in a model's production performance?

Model Product Performance Degradation

Analytics

Describing a real-world data cleaning and organization project

Data Cleaning Experiences

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

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 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?

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

Say you are tasked with analyzing how well a model fits the data given. You want to determine a relationship between two variables.

What is the downside of only using the R-Squared $(R^2)$ value to do so?

In the context of regression analysis, why is it misleading to rely solely on a high R-squared ($R^2$) value to infer a causal relationship between variables?

In the context of regression analysis, why is it misleading to rely solely on a high R-squared ($R^2$) value to infer a causal relationship between variables?

Using R Squared I

Which of the following best describes the R-squared ($R^2$) value in the context of a regression model?

Which of the following best describes the R-squared ($R^2$) value in the context of a regression model?

Using R Squared II

What is a limitation of using R-squared ($R^2$) as the only metric to evaluate a regression model's fit?

What is a limitation of using R-squared ($R^2$) as the only metric to evaluate a regression model's fit?

Using R Squared III

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.

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

Skyscanner Partner ETL

Data Pipelines

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

How would you interpret coefficients of logistic regression for categorical and boolean variables?

Why is one-hot encoding recommended for categorical variables in logistic regression models?

Why is one-hot encoding recommended for categorical variables in logistic regression models?

You’ve been tasked with building a classification machine-learning model to predict whether a transaction is either fraud or not fraud for a credit card company. You have ten years of historical data on transactions, including a flag for whether a transaction was manually identified as fraud.

Describe how you might go about building a fraud detection model for credit card transactions. Be sure to mention the possible model types, discuss the bias-variance tradeoff in model development, and address any complexities that arise from the class imbalance.

Input features in the data include:

<ul>
<li>Transaction amount</li>
<li>Merchant category for the transaction</li>
<li>Zip code for the merchant</li>
<li>Zip code for the billing address</li>
<li>Average transaction amount for the account over the past six months</li>
</ul>

Output feature:

<ul>
<li>0/1 flag for fraud (0 = not fraud, 1 = fraud)</li>
</ul>

Note: Fraudulent transactions are (thankfully) a very small percentage of all historical transactions. Assume fraudulent transactions are 0.01% of historical data.While building the model to perform the classification, you need to consider the bias/variance tradeoff, and take into account the fact that there is a class imbalance (very few of the observations are “fraud”).

What are the key differences between classification models and regression models?

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

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

Classification vs 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

How would you tackle multicollinearity in multiple linear regression?

You work as a backend engineer at Glassdoor. The company relies on multiple legacy systems to aggregate and process job postings, salary data, and company reviews. To ensure reliable data ingestion and processing, you are tasked with building a monitoring service that tracks the health of these legacy systems via regular heartbeat signals.

The service must expose one endpoint to receive heartbeat signals from each legacy system, and another endpoint to return a list of systems that have not sent a heartbeat in the last five seconds. How would you architect this monitoring system? Write a technical design document describing your approach.

Design a monitoring service for legacy system health via heartbeat endpoints at Glassdoor.

Legacy System Heartbeat Monitor

DevOps

You’re building a machine learning model to predict hospital readmission risk using patient records from the past five years. The data includes patient demographics, diagnoses, treatment history, and outcomes, but hospitals periodically change treatment protocols and data collection standards over time.

How would you structure your training, validation, and test splits to ensure your model generalizes to future patients? What risks would arise if these datasets were not clearly separated, and how would you validate that your final evaluation reflects real-world performance?

How would you structure your training, validation, and test splits to ensure your model generalizes to future patients? 

Training vs Validation vs Test Data

You are given a singly linked list, write a function to find and return the last node of the list. If the list is empty, return null.

Write a function to find and return the last node of a singly linked list. If the list is empty, return null.

Last Element of a Singly Linked List

Data Structures & Algorithms

Let’s say we’ve deployed a recommendation model for an e-commerce site, and after two months, we notice that the model’s click-through rate has dropped from 12% to 7%.

How would you investigate the cause of this performance decline, and what steps might you take to address it?

Describe a time when you had to define a long-term vision for a project or team and move it from concept to reality.

Interviews for leadership or senior technical roles look for more than just “finishing a project.” Your answer should specifically address:

<ol>
<li>The “Why”: What data or organizational gap necessitated this vision?</li>
<li>The Framework: How did you translate a high-level goal into a roadmap?</li>
<li>The Friction: How did you handle stakeholders or team members who were skeptical of the new direction?</li>
</ol>

Vision Setting and Execution Strategy

Product Sense & Metrics

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

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

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

Let’s say you are given a model that predicts whether a piece of news is relevant or not when shared on Twitter.

How would you evaluate the model?

Assume we have some credit model, which has a calibrated (up to some error) score of how credit-worthy any individual person is.

For example, if the model’s estimate is 83% then we can assume the actual score is between 81% and 85%.

If we take 83% as a score cutoff and deem everyone above that score as credit-worthy, are we over-estimating or underestimating the actual population’s credit score?

Let’s say there are two tables, <code>students</code> and <code>tests</code>.

The <code>tests</code> table doesn’t have a student id. However, it has the first and last name and date of birth, which can be inaccurate because these details are entered manually.

What process would you use to determine which student in the student rosters took which exam?

Notes: You can assume that you can utilize a human to support reviewing your matches and that you need to evaluate thousands of rows of data.

Example:

Input:

<code>students</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>firstname</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>lastname</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>date_of_birth</code></td>
<td>DATETIME</td>
</tr>
</tbody>
</table>
Output:

<code>tests</code> table

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

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

<tr>
<td><code>lastname</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>date_of_birth</code></td>
<td>DATETIME</td>
</tr>

<tr>
<td><code>test_score</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>test_date</code></td>
<td>DATETIME</td>
</tr>
</tbody>
</table>

What process would you use to determine which student took which exam?

Student Tests

Data Modeling

Let’s say you’re an ML engineer at Netflix. You have access to reviews of 10K movies. Each review contains multiple sentences along with a score ranging from 1 to 10.

How would you design an ML system to predict the movie score based on the review text?

Design an ML system to predict the movie score from the review text

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

The University Of British Columbia Interview Questions

The University Of British Columbia Interview Guides

The University Of British Columbia Interview Questions

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Discussion & Interview Experiences

Discussion & Interview Experiences