Prepare for and practice interview questions from Cargill across topics like Machine Learning, Statistics, Python and more.

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Machine Learning

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

Bagging vs Boosting

Explain the key differences between classification models and regression models

Classification and Regression

Would you use logistic regression on a dataset with 1000 samples and 10,000 features, and why or why not

1000 Sample Classifier

How would you quantify the uncertainty of a time-series forecasting model?

Quantify Uncertainty

Which clustering algorithms would you use if you have continuous AND categorical variables in your data set?

Clustering With Mixed Variable Types

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

Business Case

Friend Requests Down

How would you evaluate switching to a new vendor offering better terms after signing a long-term contract?

Alternative Vendor Tradeoff

Supply Chain

How would you redesign the supply chain and estimate financial impact after a major China tariff?

China Tariff Supply Chain

How would you handle a sole supplier demanding a steep price increase when resourcing isn’t an option?

Product Procurement Predicament

Data Pipelines

How would you clean a 100GB .csv file without loading it into memory

Processing Large CSV

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

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

Write a Python function called <code>longest_increasing_subsequence</code> that takes a list of integers as input and returns as output the length of the longest increasing subsequence in the list.

Note: An increasing subsequence is a subset of a list where the elements are in strictly increasing order. The subsequence does not have to be contiguous or unique.

<h3>Example:</h3>

Input:

<pre tabindex="0" class="chroma"><code>nums = [10, 9, 2, 5, 3, 3, 7, 101, 18]
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>4
</code></pre>

Explanation:

The longest increasing subsequence is <code>[2, 3, 7, 101]</code>, for a total length of 4.

Find the longest increasing subsequence in a list of integers.

Longest Increasing Subsequence

Data Structures & Algorithms

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’re the Spare Parts Purchasing Manager at a manufacturing company and have just signed a 5-year contract with Vendor A to supply critical, expensive parts with long lead times. Now, Vendor B approaches you offering the same parts at a lower price and with shorter lead times.

How would you decide whether to pursue Vendor B’s offer, and what factors would you weigh in making this decision?

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

Let’s say our company currently sources 80% of its electronics components from factories in China, but a new 50% tariff on Chinese imports is about to take effect.

How would you approach re-designing our supply chain to minimize costs, and what steps would you take to estimate the resulting financial impact?

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 manage procurement for an equipment manufacturer that relies on a single supplier for a specialized hydraulic valve used in your core product line. The supplier notifies us of an immediate 40% price increase. Leadership has instructed that we cannot accept any price increase, and finding a new supplier isn’t an option.

How would you approach negotiating with the supplier or managing this situation to maintain our cost structure?

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 product manager at Facebook comes to you and tells you that friend requests are down 10%.

What would you do?

You work as a data analyst on a social media platform similar to Facebook. If you observe a 10% decline in friend requests over the last three months, which analysis or metric-tracking approach could aid in uncovering the reasons?

You work as a data analyst on a social media platform similar to Facebook. If you observe a 10% decline in friend requests over the last three months, which analysis or metric-tracking approach could aid in uncovering the reasons?

Let’s say that you’re trying to run some data processing and cleaning on a <code>.csv</code> file that’s currently 100GB large.

You realize it’s too big to store in memory, so you can’t clean the file using <code>pandas.read_csv()</code>. How would you get around this issue and clean the data?

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

Describing a real-world data cleaning and organization project

Data Cleaning Experiences

Analytics

You are building a binary classifier on a dataset with 1000 samples and 10,000 features.

Would you use logistic regression? Why or why not?

Say you’re an ML engineer for a large bank. Your department already has several well-functioning forecasting models for stock price predictions. These models take in streams of data regarding stocks to predict future stock prices.

However, with any predictive model, there exists a certain degree of uncertainty. Your manager asks you to quantify the uncertainty of a current model so she has a good idea of expected success in the future.

Given a time-series forecasting model and data about past predicted and true stock prices, how would you quantify the uncertainty of the current model?

Let’s say you work at a food delivery company.

How would you measure the effectiveness of giving extra pay to delivery drivers during peak hours to meet the demand from consumers?

How would you measure the effectiveness of giving extra pay to delivery drivers during peak hours to meet consumer demand

Extra Delivery Pay

A/B Testing

Let’s say you have a data set with both continuous and categorical variables.

Which clustering algorithms would NOT be useful when dealing with this situation? Which clustering algorithms would you use instead?

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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