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Machine Learning

Interpret coefficients of logistic regression for categorical and boolean variables

Coefficients of Logistic Regression

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

Bank Fraud Model

Lasso vs Ridge

Rebalancing outcome probabilities for a classifier on imbalanced data.

Rebalance Probabilities

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

Bagging vs Boosting

Data Structures & Algorithms

Given an integer N, write a function that returns all of the prime numbers up to N

Prime to N

Given a string, write a function to find its first recurring character.

Recurring Character

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

Implement a merge sort on an array of alphanumeric strings, sorting by letters alphabetically and numbers in descending order

Descending Alphanumeric Sorting

Write a function that tests whether a string of brackets is balanced.

The Brackets Problem

Analytics

How would you present the performance of each subscription to an executive?

Analyzing Churn Behavior

How would you approach improving the quality of airline data?

Addressing Data Quality Issues

Which metric should we use for late orders if the distribution is right-skewed and what does that tell us?

Late Orders

Let's say you work at Facebook and you're analyzing churn on the platform.

Retention Rate Disparity

You’re tasked with analyzing data from multiple sources, such as payment transactions, user behavior, and fraud detection logs. How would you approach solving a data analytics problem involving these diverse datasets? What steps would you take to clean, combine, and extract meaningful insights that could improve the system's performance?

Analyzing Multiple Data Sources

Behavioral

Describing a data project and its challenges

Hurdles In Data Projects

Prioritized debt reduction, process improvement, and a focus on maintainability for fintech efficiency

Decreasing Tech Debt

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

Client Solution Pushback

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

Product Sense & Metrics

How would you verify the claim that Facebook is losing young users and what test metrics would you look at

Losing Users

What’s the expected churn rate in March for all customers that bought the product since January 1st?

Expected Churn

Google Maps Improvement

How would you resolve slow, timing-out keyword searches across multiple database fields?

Search Timeout

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?

Given an integer <code>N</code>, write a function that returns a list of all of the prime numbers up to <code>N</code>.

Note: Return an empty list there are no prime numbers less than or equal to <code>N</code>.

Example:

Input:

<pre tabindex="0" class="chroma"><code>N = 3
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>def prime_numbers(N) -&gt; [2,3]
</code></pre>

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

Give an example of the tradeoffs between the two.

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

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

Bagging vs. Boosting

Given a string, write a function <code>recurring_char</code> to find its first recurring character. Return <code>None</code> if there is no recurring character.

Treat upper and lower case letters as distinct characters.

You may assume the input string includes no spaces.

Example 1:

Input:

<pre tabindex="0" class="chroma"><code>input = &#34;interviewquery&#34;
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>output = &#34;i&#34;
</code></pre>

Example 2:

Input:

<pre tabindex="0" class="chroma"><code>input = &#34;interv&#34;
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>output = None
</code></pre>

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?

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

How would you design the system?

Note: Sketch a star schema to explain your design.

Design a data warehouse for a new online retailer

Retailer Data Warehouse

Data Modeling

You’re given a string that may contain the characters <code>{</code>, <code>}</code>, <code>[</code>, <code>]</code>, <code>(</code>, and <code>)</code>.

Task: Verify that the string is balanced. A balanced string is one where every opening character, <code>{</code>, <code>[</code>, or <code>(</code>, has a corresponding closing character, <code>}</code>, <code>]</code>, or <code>)</code>.

Write a function called <code>is_balanced(string: str) -&gt; bool</code> which verifies the balance of a string.

Example:

<pre tabindex="0" class="chroma"><code>is_balanced(&#39;(())[]{}&#39;) -&gt; True
</code></pre>

<pre tabindex="0" class="chroma"><code>is_balanced(&#39;{([(){}])()}&#39;) -&gt; True
</code></pre>

<pre tabindex="0" class="chroma"><code>is_balanced(&#39;{}[]())&#39;) -&gt; False
</code></pre>

<hr/>

What’s the difference between Lasso and Ridge Regression?

Under what circumstances is it recommended to use Lasso regression over Ridge regression?

Under what circumstances is it recommended to use Lasso regression over Ridge regression?

Let’s say you are working at Facebook and are asked to investigate the claim that Facebook is losing young users.

<ol>
<li>How would you verify this claim?</li>
<li>What test metrics would you look at?</li>
</ol>

Let’s say you are working at Facebook and are asked to investigate the claim that Facebook is losing young users.

Pick the metric that is the least relevant for your analysis.

Let’s say you are working at Facebook and are asked to investigate the claim that Facebook is losing young users.

Pick the metric that is the least relevant for your analysis.

Let’s say that you’re the PM on Google Maps.

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

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

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

Explain the key differences between classification models and regression models

Classification and Regression

What is the difference between covariance and correlation? Provide an example.

Explain the difference between covariance and correlation and provide an example

Covariance vs Correlation

Statistics

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

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

Random Forest Explanation

The support team at your company is trying to analyze ticketing data to improve response times. They have noticed that a significant number of tickets have no assigned agent, making it difficult to track the progress of these tickets.

Write a SQL query to count the total number of tickets, the number of tickets assigned to agents, and the number of tickets that are not assigned to any agent.

Note: Tickets that are not assigned to any agent have <code>NULL</code> in their <code>agent_id</code> field.

Example:

Input:

<code>tickets</code> table

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

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

<tr>
<td>issue</td>
<td>VARCHAR</td>
</tr>

<tr>
<td>agent_id</td>
<td>INTEGER</td>
</tr>

<tr>
<td>created_at</td>
<td>TIMESTAMP</td>
</tr>

<tr>
<td>updated_at</td>
<td>TIMESTAMP</td>
</tr>
</tbody>
</table>
Output:

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

<tbody>
<tr>
<td>total_tickets</td>
<td>INTEGER</td>
</tr>

<tr>
<td>tickets_with_agent</td>
<td>INTEGER</td>
</tr>

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

Count total tickets, tickets with agent assignment, and tickets without agent assignment.

Ticket Agent Analysis

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.

Note: Can you do it on \(O(n)\) time?

Note: Even though there could be many solutions, only one needs to be returned.

Example 1:

Input:

<pre tabindex="0" class="chroma"><code>array = [1 2 3 4] 
target = 5 
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>def sum_pair_indices(array, target) -&gt; [0 3] or [1 2]
</code></pre>

Example 2:

Input:

<pre tabindex="0" class="chroma"><code>array = [3]
target = 6 
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>Do NOT return [0 0] as you can&#39;t use an index twice.
</code></pre>

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?

The Fibonacci sequence is a series of numbers in which each number is the sum of the two preceding ones, usually starting with 0 and 1. It is often used in algorithm examples, and is defined by the following formula: F(n) = F(n-1) + F(n-2), with F(0) = 0 and F(1) = 1.

Your task is to implement the Fibonacci algorithm in three different methods:
1. Recursively
2. Iteratively
3. Using Memoization

Example 1:

Input:

<pre tabindex="0" class="chroma"><code>n = 5
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>fibonacci(n) -&gt; 5
</code></pre>

Example 2:

Input:

<pre tabindex="0" class="chroma"><code>n = 10
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>fibonacci(n) -&gt; 55
</code></pre>

The Fibonacci sequence starts as follows: 0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55…

This question requires the implementation of the Fibonacci sequence using three different methods: recursively, iteratively, and using memoization.

Implementing the Fibonacci Sequence in Three Different Methods

Given a <code>users</code> table, write a query to return only its duplicate rows.

Example:

Input:

<code>users</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>

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

Duplicate Rows

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.

What are some machine learning methods for building this system?

What are some machine learning methods for building this system

FAQ Matching

Let’s say we’re designing a deep learning pipeline to process millions of customer support chat logs, and we want to use a Transformer encoder layer built in PyTorch to extract useful representations for downstream tasks like sentiment classification.

How would you approach building this Transformer encoder layer from scratch—what are the essential components, how would you implement them in PyTorch, and why are elements like residual connections and layer normalization critical for stable and effective training?

How would you build and justify the components of a Transformer encoder layer in PyTorch for large-scale text data?

Transformer Encoder Layer

AI & Agentic Systems

Given a string, write a function to determine if it is palindrome or not.

Note: A palindrome is a word/string that is read the same way forward as it is backward, e.g. <code>&#39;reviver&#39;</code>, <code>&#39;madam&#39;</code>, <code>&#39;deified&#39;</code> and <code>&#39;civic&#39;</code> are all palindromes, while <code>&#39;tree&#39;</code>, <code>&#39;music&#39;</code> and <code>&#39;person&#39;</code> are not palindromes.

Example:

Input:

<pre tabindex="0" class="chroma"><code>word1 = &#34;tree&#34;
word2 = &#34;radar&#34;
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>def is_palindrome(word1) -&gt; False
def is_palindrome(word2) -&gt; True
</code></pre>

Given a string, write a function to determine if it is palindrome or not.

String Palindromes

You are given an array of strings <code>arr</code>, where each string is in the format: <code>&lt;letters&gt;&lt;number&gt;</code>. For example, you might see <code>&#34;a1&#34;</code>, <code>&#34;b32&#34;</code>, or <code>&#34;caba522&#34;</code>.

Write a function called <code>custom_sort(arr)</code>, which sorts the list of strings in two ways: first by the letter, in alphabetical order, and second by the numbers in descending order.

Note: The letters can only be lower case, and have length \(≥1\).

Example:

Input:

<pre tabindex="0" class="chroma"><code>arr = [&#34;b12&#34;, &#34;a15&#34;, &#34;b7&#34;, &#34;a3&#34;, &#34;c4&#34;, &#34;c20&#34;]
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>custom_sort(arr) -&gt; &#39;[&#34;a15&#34;, &#34;a3&#34;, &#34;b12&#34;, &#34;b7&#34;, &#34;c20&#34;, &#34;c4&#34;]&#39;
</code></pre>

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.

How would you go about diagnosing and improving the performance of this query?

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

Slow SQL Query

Query Optimization

Imagine you are asked to build a machine learning model to decide new loan approvals for a
financial firm. You ask the data department in the company for a subset of data to get started
working on the problem. The data includes different features about applicants such as age,
occupation, zip code, height, number of children, favorite color, etc. You decide to build
multiple machine learning models to test out different ideas before settling on the best one.

How would you explain the bias-variance tradeoff with regards to
building and choosing a model to use?

Bias vs. Variance Tradeoff

Let’s say you’re a senior data analyst and your team is tasked with addressing data quality issues for an airline company. Customer records, booking details, and flight schedules often have missing fields, duplicate entries, or incorrect data.

How would you approach improving the quality of this data and ensuring it remains clean and reliable over time?

Let’s say we’re launching a new version of the PayPal app, but due to a tight deadline, we don’t have time to run the entire suite of 300 automated test cases before release.

How would you handle this situation?

How would you approach limited testing time before a major app release?

Testing Constraints

A/B Testing

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 you’re working with a dataset of user-level events for a new product feature, and you notice issues like missing values, duplicate records, inconsistent data types, and some clear outliers. You need to prepare this data for training a machine learning model.

How would you design a data processing pipeline using Pandas and NumPy to clean and validate the data before modeling, and what steps would you take to ensure the pipeline is reusable and reliable for future use?

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

User Event Data Pipeline

Data Pipelines

Let’s say you work for a bank that gives out personal loans. Your co-worker develops a model that takes in customer inputs and returns if a loan should be given or not.

<ol>
<li>What kind of model did the co-worker develop? 
</li>
<li>Another co-worker thinks they have developed a better model to predict defaults on the loans. Given that personal loans are monthly installments of payments, how would you measure the difference between the two credit risk models within a timeframe? 
</li>
<li>What metrics would you track to measure the success of the new model?</li>
</ol>

What kind of model did the co-worker develop, how would you measure the difference between the two credit risk models within a timeframe, and what metrics would you track to measure the success of the new model

Loan Model

Imagine you run a pizza franchise, and you run into a problem with a lot of no-shows after customers place their order.

What features would you include in a model to try to predict a no-show?

What features would you include in a model to predict a no-show

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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