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Precision Castparts Interview Questions

precision castparts corp. is a worldwide, diversified manufacturer of complex metal components and products. it serves the aerospace, power generation, and general industrial markets. pcc is the market leader in manufacturing large, complex structural investment castings, airfoil castings, and forged components used in jet aircraft engines and industrial gas turbines. the company is also a leading producer of highly engineered, critical fasteners for aerospace, and other general industrial markets and supplies metal alloys and other materials to the casting and forging industries.

precision castparts corp. is a worldwide, diversified manufacturer of complex metal components and products. it serves the aerospace, power generation, and general industrial markets. pcc is the market leader in manufacturing large, complex structural investment castings, airfoil castings, and forged components used in jet aircraft engines and industrial gas turbines. the company is also a leading producer of highly engineered, critical fasteners for aerospace, and other general industrial markets and supplies metal alloys and other materials to the casting and forging industries.

Overview

Precision Castparts Interview Guides

Machine Learning

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

Bank Fraud Model

Rebalancing outcome probabilities for a classifier on imbalanced data.

Rebalance Probabilities

Lasso vs Ridge

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

Bagging vs Boosting

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

Data Structures & Algorithms

Given two sorted lists, write a function to merge them into one sorted list.

Merge Sorted Lists

Write a program in python to determine the TF values for each term of a document

Term Frequency

Implement a fixed-length array with addition, deletion, and search operations.

Fixed-Length Arrays: Deletion

Cyclic Detection

Write a function that returns the number of triplets in the array that sum to `k`.

Triplet Counting

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

Statistics

Explain the difference between covariance and correlation and provide an example

Covariance vs Correlation

What does it mean to "bootstrap" a data set?

Bootstrapping Samples

Business Case

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

Deciding Between Solutions

As a data scientist at a mortgage bank, how would you approach building a predictive model for loan default risk?

Risk Model for a Mortgage Bank

Challenge

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 two sorted lists, write a function to merge them into one sorted list.

Bonus: What’s the time complexity?

Example:

Input:

<pre tabindex="0" class="chroma"><code>list1 = [1,2,5]
list2 = [2,4,6]
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>def merge_list(list1,list2) -&gt; [1,2,2,4,5,6]
</code></pre>

You have two sorted lists, one with m elements and one with n elements. What would be the time complexity of merging the two lists via merge sort?

You have two sorted lists, one with m elements and one with n elements. What would be the time complexity of merging the two lists via merge sort? 

Merging Lists Complexity

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?

Describing a data project and its challenges

Hurdles In Data Projects

Behavioral

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?

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 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>

Imagine you have been given an array of integers, and a query number <code>k</code>. Your task is to write a function that finds all the triplets in the array that sum up to the query number <code>k</code>.

Let’s illustrate this with some examples:

Example 1:

Input:

<pre tabindex="0" class="chroma"><code>nums = [1, 2, 3, 4, 5]
k = 6
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>count_triplets(nums, k) -&gt; 1
</code></pre>

In this case, there is only one triplet that sums up to 6: (1, 2, 3).

Example 2:

Input:

<pre tabindex="0" class="chroma"><code>nums = [10, 10, 20, 30, 40]
k = 60
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>count_triplets(nums, k) -&gt; 3
</code></pre>

In this case, there are three triplets that sum up to 60: (10, 10, 40), (10, 20, 30), and (10, 20, 30).
The value 10 can be retrieved as <code>nums[0]</code> or <code>nums[1]</code>, therefore resulting in two triplets with the same value.

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?

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

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

Given a <code>ListNode</code> representing the head of a linked list, write a function <code>is_cyclic</code> which returns <code>True</code> if a linked list is cyclic and <code>False</code> if it is not. A linked list is cyclic when there is no tail to the linked list, and the supposed tail is attached to another node inside the list itself, creating a cycle.

A <code>ListNode</code> is defined as:

<pre tabindex="0" class="chroma"><code>class ListNode:
	value: ListNode = None
 next: ListNode = None
</code></pre>

Example:

Input:

<img src="/s3/questions/1a65651d-a483-4481-b073-2738d65ab9d7.png" alt="image"/>

Output:

<pre tabindex="0" class="chroma"><code>def is_cyclic(head) -&gt; True
</code></pre>

Say you are given a text document in the form of a string with the following sentences:

Example:

Input:

<pre tabindex="0" class="chroma"><code>document = &#34;I have a nice car with a nice tires&#34;
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>
{
&#34;I&#34;:0.11,
&#34;have&#34;:0.11,
&#34;a&#34;:0.22,
&#34;nice&#34;:0.22,
&#34;car&#34;: 0.11,
&#34;with&#34;:0.11,
&#34;tires&#34;:0.11
}
</code></pre>

Write a program in python to determine the TF (<code>term_frequency</code>) values for each term of this document.

Note: Round the term frequency to 2 decimal points.

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 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

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

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

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

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

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

Pizza No Show

You work as the sole machine learning engineer at a mid-sized e-commerce company. You have developed a predictive model for customer purchase likelihood that needs to be retrained periodically and reliably served in production. There is no dedicated data engineering or ML-Ops support.

How would you automate the retraining and deployment of this model while ensuring reliability and minimal downtime?

Design an end-to-end retraining and production pipeline for a machine learning model without dedicated infrastructure or support staff.

Solo ML Deployment

ML Ops

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

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

What do you tell an interviewer when they ask you what your strengths and weaknesses are?

Your Strengths and Weaknesses

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?

You work as an ML engineer for a large company. The company wants to implement a machine learning system that utilizes facial recognition to facilitate employee clock-in, clock-out, and access to secure systems. The company also hires temporary contract consultants who need to be able to use the system.

How would you design this system?

Designing a secure and user-friendly facial recognition system for employee management while prioritizing privacy and ethical considerations

Distributed Authentication Model

Concurrent & Distributed Systems

Important: You must answer <a href="https://www.interviewquery.com/questions/array-operations-fixed-length-arrays-1">this question</a> before attempting this one. We recommend copying your previous solution and iterating the new solution with the added operations.

Create a more extensive <code>Array</code> class simulating the functionality of fixed-size arrays. The array’s fixed size is <code>6</code>. In this question, we will be implementing the deletion and search operations.

<ul>
<li><code>pop_back() -&gt; element</code> - removes the last element of the array. Returns the removed element. If the array is empty, return <code>None</code>.</li>

<li><code>pop_front() -&gt; element</code> - removes the first element of the array. Returns the removed element. If the array is empty, return <code>None</code>.</li>

<li><code>pop(index) -&gt; element</code> - removes the element at the specified index and returns it. When the index is not in range, raise an <code>IndexError</code>.</li>

<li><code>search(element) -&gt; index</code> - searches for the specified element and returns the index of the first instance if it exists inside the array. Returns <code>-1</code> if the element does not exist inside the array. Example: <code>Array([1, 2, 3]).search(2) -&gt; 1</code>.</li>

<li><code>__contains__(element) -&gt; bool</code> - checks if the element exists inside the array. Returns <code>True</code> if it exists and <code>False</code> if it does not.</li>

<li><code>abolish(element) -&gt; count</code> - removes all instances of an element and returns the count of all removed instances.</li>

<li><code>__eq__(other) -&gt; bool</code> - checks the equality of the array with another array called <code>other</code>. Raises a <code>TypeError</code> if <code>other</code> is not an instance of this custom <code>Array</code> class.</li>
</ul>

Note: All the basic addition operations must be implemented first. (Refer to Fixed Length Arrays 1)

Let’s say you’re trying to calculate churn for a subscription product.

You noticed that of all customers that bought subscriptions in January 2020, about 10% of them canceled their membership before their next cycle on February 1st.  

If you assume that your new customer acquisition is uniform throughout each month and that customer churn goes down by 20% month over month, what’s the expected churn rate in March for all customers that bought the product since January 1st?

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

Expected Churn

Product Sense & Metrics

Let’s say you work as a data scientist at DoorDash. 

You are tasked to build a machine learning system that minimizes missing or wrong orders placed on the app.

How would you go about designing this system?

Minimize Wrong Orders

Imagine you are trying to track the position of a moving car using noisy GPS data, how would you explain the Kalman filter to someone with no technical background?

Use a real-world analogy to make your explanation accessible.

Explain the Kalman filter in simple, real-world terms.

Kalman Filter in GPS tracking

Forecasting & Time Series

Let’s say that you work for a company like Netflix. Netflix has two pricing plans: $15/month or $100/year.

Let’s say an executive at the company wants you to analyze the churn behavior of users that subscribe to either plan.

What kinds of metrics / graphs / models would you build to help give the executive an over-arching view of how the subscriptions are performing?

Let’s say that you’re a data scientist at DoorDash.

How would you build a model to predict which merchants the company should go after for acquisition when entering a new market?

How to model merchant acquisition in a new market?

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

Precision Castparts Interview Questions

Overview

Precision Castparts Interview Guides

Challenge

Precision Castparts Interview Questions

Discussion & Interview Experiences

Discussion & Interview Experiences