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DNV Interview Questions

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Machine Learning

Bagging vs Boosting

Replaced by QUESTION 791 - Should be deleted

Bias Variance Tradeoff

PCA and K-Means

Bias vs. Variance Tradeoff

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

Overfit Avoidance

Data Structures & Algorithms

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

Prime to N

Transform a dataframe containing a list of user IDs and their full names into one that contains only the user ids and the first name of each user.

First Names Only

This problem involves identifying duplicate numbers in a list of integers. The function should return a list of the duplicate numbers.

Find Duplicate Numbers in a List

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

The Brackets Problem

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

Supply Chain

How would you decide on a metric and approach for worker allocation across an uneven production line?

Worker Distribution Dilemma

Design Patterns

Create a text editor while displaying method overriding as a concept.

Text Editor With OOP

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

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 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 that you’re training a classification model.

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

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/>

Given a list of integers, identify all the duplicate values in the list. Assume that the list can contain both positive and negative numbers, and the order of the list does not matter. A number is considered a duplicate if it appears more than once in the list. Return a list of the duplicate numbers.

Example 1:

Input:

<pre tabindex="0" class="chroma"><code>nums = [1, 2, 3, 1, 2, 3]
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>find_duplicates(nums) -&gt; [1, 2, 3]
</code></pre>

The numbers 1, 2, and 3 all appear more than once in the list, so they are considered duplicates.

Example 2:

Input:

<pre tabindex="0" class="chroma"><code>nums = [1, -1, 2, 3, 3, -1]
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>find_duplicates(nums) -&gt; [-1, 3]
</code></pre>

The numbers -1 and 3 both appear more than once in the list, so they are considered duplicates. Note that the order of the output does not matter.

Example 3:

Input:

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

Output:

<pre tabindex="0" class="chroma"><code>find_duplicates(nums) -&gt; []
</code></pre>

None of the numbers in the list appear more than once, so there are no duplicates.

You’re AI engineer working on an energy forecasting system at Siemens that helps utilities predict short-term electricity demand to balance grid load and reduce outages. You train a recurrent neural network on long historical time series (weather, calendar effects, local demand signals) to forecast energy demand.

Early in training, the model appears to learn normally, but after several epochs the loss becomes highly unstable, oscillating wildly before turning into NaNs. At the same time, training slows dramatically and the model fails to recover even after restarting from recent checkpoints.

You’ve verified that the data pipeline is correct and labels are well-aligned with inputs. No changes were made to the dataset, but increasing the sequence length or learning rate makes the problem significantly worse.

How would you investigate the cause of this training instability? What signals would you look for during debugging, and how would those findings guide the changes you make to the model or training setup?

How would you investigate the cause of this training instability?

Training Instability in Neural Networks

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

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?

Let’s say we’re managing a production line for assembling electric scooters, with 10 stations where each has a different average processing time per unit (for example, Station 1 takes 5 minutes, Station 2 takes 3 minutes, and so on).

How would you determine the right metric to use for balancing worker allocation across the stations, and how would you use it to figure out how many workers each station needs?

You’re given a dataframe containing a list of user IDs and their full names (e.g. ‘James Emerson’).

Transform this dataframe into a dataframe that contains the user ids and only the first name of each user.

Example:

Input:

<table>
<thead>
<tr>
<th>user_id</th>
<th>name</th>
</tr>
</thead>

<tbody>
<tr>
<td>1034</td>
<td>James Emerson</td>
</tr>

<tr>
<td>9430</td>
<td>Fiona Woodward</td>
</tr>

<tr>
<td>7281</td>
<td>Alvin Gross</td>
</tr>

<tr>
<td>5264</td>
<td>Deborah Handler</td>
</tr>

<tr>
<td>8995</td>
<td>Leah Xue</td>
</tr>
</tbody>
</table>
Output:

<table>
<thead>
<tr>
<th>user_id</th>
<th>name</th>
</tr>
</thead>

<tbody>
<tr>
<td>1034</td>
<td>James</td>
</tr>

<tr>
<td>9430</td>
<td>Fiona</td>
</tr>

<tr>
<td>7281</td>
<td>Alvin</td>
</tr>

<tr>
<td>5264</td>
<td>Deborah</td>
</tr>

<tr>
<td>8995</td>
<td>Leah</td>
</tr>
</tbody>
</table>

Describe a data project you worked on. What were some of the challenges you faced?

You have been tasked with designing three classes: <code>text_editor</code>, <code>moving_text_editor</code>, and <code>smart_text_editor</code>. These classes are to be created with specific functionalities as defined below:

<ol>
<li><code>text_editor</code> class:

<ul>
<li><code>write_line(string:str)</code>: A method which appends a given string to the end of the existing string.</li>
<li><code>delete_line(char_num : int)</code>: A method which deletes <code>char_num</code> number of characters from the existing string, starting from the end. If there are no characters left, the method should do nothing.</li>
<li><code>special_operation()</code>: A method which currently does nothing.</li>
<li><code>get_notes()</code>: A method which returns the internal string.</li>
</ul></li>

<li><code>moving_text_editor</code> class:

<ul>
<li>This class extends <code>text_editor</code>. The <code>special_operation()</code> method is overridden. Initially, the cursor will be at the end of the current string. If <code>special_operation()</code> is called, it moves the cursor to the beginning of the string, any additional appends will be appended to the beginning of the string instead. Calling <code>special_operation()</code> again reverses the cursor operation.</li>
</ul></li>

<li><code>smart_text_editor</code> class:

<ul>
<li>This class extends <code>text_editor</code>. In this class, the <code>special_operation()</code> method is overridden to serve as an undo operation, allowing it to undo an infinite number of operations.</li>
</ul></li>
</ol>

<h3>Input</h3>

<pre tabindex="0" class="chroma"><code>[[&#39;special_text_editor&#39;], [&#39;write_line&#39;, &#39;special_operation&#39;, &#39;write_line&#39;], [&#39;World&#39;, &#39;Hello, &#39;]]
</code></pre>

<h3>Output</h3>

<code>&#34;Hello, World&#34;</code>

How would you explain the bias variance tradeoff in machine learning to a high school student?

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

What’s the relationship between PCA and K-means clustering?

What does the variable “k” in k-means clustering refer to?

What does the variable "k" in k-means clustering refer to?

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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