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Publix Super Markets Interview Questions

founded in 1930, publix super markets is the largest and fastest-growing employee-owned supermarket chain in the united states. we are privately-owned, hold no long-term debt, have avoided layoffs, and continue to grow year after year. publix and our associates excel in community involvement, volunteerism and environmental sustainability. our collective commitment to diversity has contributed to our success in being a great place to work and shop.

founded in 1930, publix super markets is the largest and fastest-growing employee-owned supermarket chain in the united states. we are privately-owned, hold no long-term debt, have avoided layoffs, and continue to grow year after year. publix and our associates excel in community involvement, volunteerism and environmental sustainability. our collective commitment to diversity has contributed to our success in being a great place to work and shop.

Overview

Publix Super Markets Interview Guides

Machine Learning

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

Bagging vs Boosting

What kind of model would we build and what features would we use

Food Prep Features

When should you use regularization versus cross-validation

Regularization and Validation

Replaced by QUESTION 791 - Should be deleted

Bias Variance Tradeoff

Design the YouTube video recommendation system and explain important factors to keep in mind

Youtube Recommendations

Data Pipelines

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

Resumable Fact Table Load

Design a secure, real-time, scalable portfolio management system with notifications and caching.

Portfolio Platform Architecture

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

Scalable Data Pipelines

Analytics

How would you approach improving the quality of airline data?

Addressing Data Quality Issues

Let's say that you work at TikTok. The goal for the company next quarter is to increase the daily active users metric (DAU).

Best DAU

Analyzing Store Performance

Behavioral

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

Write a query to show the number of users, transactions, and total order amount per month in 2020

Monthly Customer Report

Write a query to generate a shopping list that sums up the total mass of each grocery item required across three recipes.

Generate Shopping List from Recipes

How would you design a data warehouse for a e-commerce company looking to expand internationally?

International e-Commerce Warehouse

Challenge

Write a query to show the number of users, number of transactions placed, and total order amount per month in the year 2020. Assume that we are only interested in the monthly reports for a single year (January-December).

Example:

Input:

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

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

<tr>
<td><code>product_id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>quantity</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
<code>products</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>price</code></td>
<td>FLOAT</td>
</tr>
</tbody>
</table>
<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>sex</code></td>
<td>VARCHAR</td>
</tr>
</tbody>
</table>
Output:

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

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

<tr>
<td><code>num_customers</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>num_orders</code></td>
<td>INTEGER</td>
</tr>

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

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>

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

Recurring Character

Data Structures & Algorithms

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

Let’s say you’re a Product Data Scientist at Instagram. How would you measure the success of the Instagram TV product?

What is the primary goal of Instagram TV as a product feature?

What is the primary goal of Instagram TV as a product feature?

Instagram TV Success

How would you measure the success of the Instagram TV product

Product Sense & Metrics

Let’s say you have a time series dataset grouped monthly for the past five years.

How would you find out if the difference between this month and the previous month was significant or not?

How would you find out if the difference between this month and the previous month was significant or not

Significance Time Series

Forecasting & Time Series

You are a chef preparing for an important event. You downloaded three recipes to make and imported each as a separate table: <code>recipe1</code>, <code>recipe2</code>, <code>recipe3</code> into your database. Each recipe requires some grocery items in various quantities.

Write a query that sums up the total mass of each grocery item required across all recipes.

Input:

<code>recipe1</code> table:

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

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

<tr>
<td><code>mass</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
<code>recipe2</code> table:

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

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

<tr>
<td><code>mass</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
<code>recipe3</code> table:

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

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

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

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

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

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

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

What are the assumptions of linear regression?

Assumptions of Linear Regression

Statistics

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.

How would you architect this loading process?

What are time series models? Why do we need them when we have less complicated regression models?

What can happen if a traditional regression model is used on data that has autocorrelation?

What can happen if a traditional regression model is used on data that has autocorrelation?

Why Do We Need Time Series Models? I

Why can't standard regression models be used effectively for data points where current values are influenced by past values?

Why can't standard regression models be used effectively for data points where current values are influenced by past values?

Why Do We Need Time Series Models? II

Explain what time series models are and why they’re needed instead of simpler regression models

Why Do We Need Time Series Models?

Given a list of <code>stock_prices</code> in ascending order by <code>datetime</code>, and their respective dates in list <code>dts</code>, write a function <code>max_profit</code> that outputs the max profit by buying and selling at a specific interval and start and end dates to buy and sell for max profit.

Example:

Input:

<pre tabindex="0" class="chroma"><code>stock_prices = [10,5,20,32,25,12]
dts = [
 &#39;2019-01-01&#39;, 
 &#39;2019-01-02&#39;,
 &#39;2019-01-03&#39;,
 &#39;2019-01-04&#39;,
 &#39;2019-01-05&#39;,
 &#39;2019-01-06&#39;,
]

def max_profit(stock_prices,dts) -&gt; 27
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>def max_profit(stock_prices, dts) -&gt; 
(27, &#39;2019-01-02&#39;, &#39;2019-01-04&#39;)
</code></pre>

Write a function to find the best days to buy and sell a stock and the profit you generate from the sale.

Buy or Sell

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

You work as a software engineer at Morningstar. You are tasked with architecting a web-based portfolio management platform where authenticated users can view their investment portfolios, including stocks, mutual funds, and other assets. The system must display real-time asset prices, send notifications for significant price drops or new recommendations from external providers, and handle large volumes of user traffic with scalable caching.

How would you architect this system?

You work as a data scientist for a grocery store chain that has a mobile app. At the end of the checkout process on the app, users are presented with an up-sell carousel, a sliding display of items that users can scroll/swipe to view and add to their cart. Currently, the carousel only presents items from the store’s personal brand.

How would you determine whether the carousel should replace store-brand items with national-brand products of the same type?

How would you determine whether the carousel should replace store-brand items with national-brand products of the same type?

Upsell Carousel

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 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 we’re designing a workflow to update user account balances in a banking app, where each transfer involves deducting $100 from one account and adding it to another.

How would you implement this as an SQL transaction, and would you use optimistic or pessimistic locking to handle potential race conditions? Why?

How would you implement a balance transfer as an SQL transaction, and decide between optimistic or pessimistic locking?

Optimistic vs Pessimistic Locking

Concurrent & Distributed Systems

Let’s say you’re tasked with building the YouTube video recommendation algorithm.

How would you design the recommendation system?

What are important factors to keep in mind when building the recommendation algorithm?

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

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

Xgboost vs Random Forest

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?

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 we’ve proposed a new analytics dashboard to a client, but after reviewing our design and data sources, the client says the solution doesn’t meet their needs and pushes back on several key features.

How would you approach the conversation to negotiate alternative solutions, and what steps would you take to ensure disagreements are handled constructively so we can still reach a successful outcome?

Let’s say you work for an e-commerce company. Vendors can send products to the company’s warehouse to be listed on the website. Users are able to order any in-stock products and submit returns for refunds if they’re not satisfied.

The front end of the website includes a vendor portal that provides sales data in daily, weekly, monthly, quarterly, and yearly intervals.

The company wants to expand worldwide. They put you in charge of designing its end-to-end architecture, so you need to know what significant factors you’ll need to consider. What clarifying questions would you ask?

What kind of end-to-end architecture would you design for this company (both for ETL and reporting)?

You work as a cloud architect at a large enterprise. The company is migrating its core web application to Microsoft Azure and wants to ensure high availability and scalability. You are asked to provision the infrastructure as code, leveraging Kubernetes for orchestration and Terraform for automation.

How would you architect this system to maximize reliability and scalability?

Write a technical design document describing your approach.

Design a highly available, scalable Azure infrastructure using Terraform and Kubernetes.

Azure Kubernetes Infrastructure

DevOps

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

A manager in a packet filling line is complaining that their machine is not functioning correctly. The machine weighs a group of packets at once and attempts to get 25 packets into a box.

He has been getting complaints from customers that the boxes contain amounts other than 25. How would you help out?

How would you help ensure each box contains exactly 25 packets

Incorrect Packets

Business Case

Let’s say we’re building a recommendation system for an e-commerce site, and we have two viable approaches: a collaborative filtering model that takes 2 days to implement, and a deep learning model that could boost accuracy by 10% but requires 3 weeks of engineering time.

How would you decide which solution to pursue, and what factors would you weigh in your decision?

How would you evaluate and choose between a fast, simple model and a slower, more accurate one for product recommendations?

Deciding Between Solutions

Let’s say we’re responsible for deploying a new version of Uber’s routing system, and the release includes both backend and frontend changes that may impact user workflows.

How would you design the deployment process to minimize risk, ensure clear communication with both clients and internal teams, and handle rollbacks if unexpected issues arise?

How would you structure a safe deployment process with effective stakeholder communication and rollback plans?

Safe Deployments

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 you run a small store that’s required to keep at least 6 bottles of water on display at all times. Right now, you have 12 bottles in stock, and you typically sell 2 bottles per day.

How many bottles would you need to order today to ensure you always meet the display requirement for the next 14 days, assuming you don’t get any additional deliveries in that period?

How many water bottles should you order to maintain a minimum display stock for two weeks?

Water Bottle Inventory

Supply Chain

Let’s you’re tasked with pitching a new feature for Google Home. Your co-worker comes to you with an idea to build a game feature for Google Home.

How would you go about deciding whether Google should build it?

How would you decide whether Google should build a game feature for Google Home

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

Publix Super Markets Interview Questions

Overview

Publix Super Markets Interview Guides

Challenge

Publix Super Markets Interview Questions

Discussion & Interview Experiences

Discussion & Interview Experiences