Prepare for and practice interview questions from Solidigm.

Solidigm Interview Questions

Solidigm Interview Guides

Machine Learning

Missing Housing Data

Why would one algorithm generate different success rates with the same dataset?

Same Algorithm Different Success

Food Delivery Times

Decision Tree Evaluation

Random Forest Explanation

Data Structures & Algorithms

Find the missing integer from a array of consequtive integers

Find the Missing Number

One Element Removed

Valid Anagram

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

Fixed-Length Arrays: Deletion

Implement a dynamic recursive fibonacci function.

Impossibly Iterative Fibonacci

Statistics

What are the assumptions of linear regression?

Assumptions of Linear Regression

Using R Squared

Covariance vs Correlation

Correlation in Regression

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

Bootstrapping Samples

Analytics

Create and write queries for health metrics for stack overflow 

Community Health Metrics

Challenges of specific student test score layouts, recommended formatting changes for enhanced analysis, and common issues found in "messy" datasets.

Digitizing Student Test Scores

Describing a data project and its challenges

Hurdles In Data Projects

Strategically resolving misaligned expectations with stakeholders for a successful project outcome

Stakeholder Communication

A/B Testing

Experimental rewards system and ways to improve it

Reward Experiment

The role of A/B testing in measuring the success rate of an analytics experiment

Success Measurement

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

Brainteasers

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

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

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

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

Talk about a time when you had trouble communicating with stakeholders. How were you able to overcome it?

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

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

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

Xgboost vs Random Forest

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

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

Data Pipelines

Create a class named <code>LRUCache</code> that implements the behavior expected of a Least Recently Used (LRU) Cache.

The <code>LRUCache</code> class implements the following methods:

<ul>
<li><code>__init__(self, capacity)</code> - initializes the object and takes in a capacity parameter specifying the maximum capacity of the cache.</li>
<li><code>get_if_exists(self, key) -&gt; Any|None</code> - gets a value based on a key. If the key exists, returns the associated value. If the key has expired or did not exist in the first place, returns <code>None</code>.</li>
<li><code>put(self, key, value)</code> - places a value inside the cache. In the case wherein the cache is full, invalidates the least recently used element. When two keys collide, the older value should be invalidated in place of the new one.</li>
</ul>

Example:

<pre tabindex="0" class="chroma"><code>cache = LRUCache(2)
cache.put(&#34;sample&#34;, 55)
cache.get_if_exists(&#34;sample&#34;) # returns 55
cache.get_if_exists(&#34;key&#34;) # returns None
cache.put(&#34;hello&#34;, 10) 
cache.put(&#34;sample&#34;, 9)
cache.put(&#34;world&#34;, 5)
cache.get_if_exists(&#34;hello&#34;) # returns None
</code></pre>

Notes:

<ul>
<li>It is assured that the value in the <code>put</code> method will never receive a <code>None</code> value. Moreover, it is assured that the capacity will always be &gt; 0.</li>

<li>All operations must be O(1).</li>

<li>Aside from Python’s standard dictionary implementation (<code>{}</code>), you may not use any other built-in data structures.</li>
</ul>

LRU Cache 1

Regularization and cross-validation are two common techniques used to improve the performance of machine learning algorithms.

When should you use one versus the other?

Given the choices below, which one is an example of a scenario where we should be using regularization?

Given the choices below, which one is an example of a scenario where we should be using regularization?

Regularization Example

Regularization and Validation

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

You are tasked with building a decision tree model to predict if a borrower will pay back a personal loan they are taking out.

<ol>
<li>How would you evaluate whether using a decision tree algorithm is the correct model for the problem?</li>

<li>Let’s say you move forward with the decision tree model. How would you evaluate the performance of the model before deployment and after?</li>
</ol>

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

Replaced by QUESTION 791 - Should be deleted

Bias Variance Tradeoff

How do you detect and handle correlation between variables in linear regression? What will happen if you ignore the correlation in the regression model?

Given two strings, write a function to return <code>True</code> if the strings are anagrams of each other and <code>False</code> if they are not.

Note: A word is not an anagram of itself.

Example 1:

Input:

<pre tabindex="0" class="chroma"><code>string_1 = &#34;listen&#34;
string_2 = &#34;silent&#34;
</code></pre>

Output:

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

Example 2:

Input:

<pre tabindex="0" class="chroma"><code>string_1 = &#34;banana&#34;
string_2 = &#34;bandana&#34;
</code></pre>

Output:

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

You have an array of integers, <code>nums</code> of length <code>n</code> spanning <code>0</code> to <code>n</code> with one missing. Write a function <code>missing_number</code> that returns the missing number in the array.

Note: Complexity of $O(n)$ required.

Example:

Input:

<pre tabindex="0" class="chroma"><code>nums = [0,1,2,4,5] 
missing_number(nums) -&gt; 3
</code></pre>

We want to build a model to predict housing prices in the city of Seattle. We’ve scraped 100K sold listings over the past three years but found that around 20% of the listings are missing square footage data.

How do we deal with the missing data to construct our model?

We want to build a model to predict housing prices in the city of Seattle. We’ve scraped 100K sold listings over the past three years, but have discovered that around 20% of the listings are missing square footage data.

How would you approach dealing with this missing data, in order to construct the most useful predictive model possible?

We want to build a model to predict housing prices in the city of Seattle. We’ve scraped 100K sold listings over the past three years, but have discovered that around 20% of the listings are missing square footage data.

How would you approach dealing with this missing data, in order to construct the most useful predictive model possible?



How would you choose the k value when using k-means clustering?

choosing k value during k-means clustering

Choosing k

Write a function <code>fib</code> which takes an integer <code>n</code> and returns the nth fibonacci number. For this question, <code>fib</code> MUST be recursively defined.

Note: <code>for</code> and <code>while</code> keywords are disabled for this question, as well as <code>functools</code>. Any workarounds around this restriction is prohibited.

Example:

Input:

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

Output:

<pre tabindex="0" class="chroma"><code>def fib(n) -&gt; 3
# the sequence is [1, 1, 2, 3, 5, 8...]
</code></pre>

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="https://d2qpirhrfplx04.cloudfront.net/1a65651d-a483-4481-b073-2738d65ab9d7.png" alt="image"/>

Output:

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

Cyclic Detection

Find if there is a path from a starting point to an ending point in a walled maze

Pathfinder in Maze

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

Why Do We Need Time Series Models?

Forecasting & Time Series

Given a 2-D matrix P of predicted values and actual values, write a function precision_recall to calculate precision and recall metrics.

Return the ordered pair (precision, recall).

Example:

Input:

<pre tabindex="0" class="chroma"><code>
P = [[121, 9],
 [17, 144]]
</code></pre>

where P represents the following table

<table>
<thead>
<tr>
<th></th>
<th>Predicted =TRUE</th>
<th>Predicted =FALSE</th>
</tr>
</thead>

<tbody>
<tr>
<td>Actual =TRUE</td>
<td>121</td>
<td>9</td>
</tr>

<tr>
<td>Actual =FALSE</td>
<td>17</td>
<td>144</td>
</tr>
</tbody>
</table>
Output:

<pre tabindex="0" class="chroma"><code>def precision_recall(P) -&gt; (0.931, 0.877,)
</code></pre>

Write a function to calculate precision and recall metrics.

Precision and Recall

Let’s say you’re working on a random forest model.

If the number of trees in a random forest are increased sequentially, will the accuracy of the model continue to increase?

Random Forest Expansion

Describe an analytics experiment that you designed. How were you able to measure success?

Say you are tasked with analyzing how well a model fits the data given. You want to determine a relationship between two variables.

What is the downside of only using the R-Squared $(R^2)$ value to do so?

In the context of regression analysis, why is it misleading to rely solely on a high R-squared ($R^2$) value to infer a causal relationship between variables?

In the context of regression analysis, why is it misleading to rely solely on a high R-squared ($R^2$) value to infer a causal relationship between variables?

Using R Squared I

Which of the following best describes the R-squared ($R^2$) value in the context of a regression model?

Which of the following best describes the R-squared ($R^2$) value in the context of a regression model?

Using R Squared II

What is a limitation of using R-squared ($R^2$) as the only metric to evaluate a regression model's fit?

What is a limitation of using R-squared ($R^2$) as the only metric to evaluate a regression model's fit?

Using R Squared III

Build a random forest model from scratch with the following conditions:

<ul>
<li>The model takes as input a dataframe <code>data</code> and an array <code>new_point</code> with length equal to the number of fields in the <code>data</code></li>
<li>All values of both <code>data</code> and <code>new_point</code> are <code>0</code> or <code>1</code>, i.e., all fields are dummy variables and there are only two classes</li>
<li>Rather than randomly deciding what subspace of the data each tree in the forest will use like usual, make your forest out of decision trees that go through every permutation of the value columns of the data frame and split the data according to the value seen in <code>new_point</code> for that column</li>
<li>Return the majority vote on the class of <code>new_point</code></li>
<li>You may use <code>pandas</code> and <code>NumPy</code> but NOT <code>scikit-learn</code></li>
</ul>

Bonus: The <code>permutations</code> in the <code>itertools</code> package can help you easily get all of any iterable object.

Example:

Input:

<pre tabindex="0" class="chroma"><code>new_point = [0,1,0,1]
print(data)
...
 Var1 Var2 Var3 Var4 Target
0 1.0 1.0 1.0 0.0 1
1 0.0 0.0 0.0 0.0 0
2 1.0 0.0 1.0 0.0 0
3 0.0 1.0 1.0 1.0 1
4 1.0 0.0 1.0 0.0 0
.. ... ... ... ... ...
95 0.0 1.0 0.0 1.0 0
96 1.0 1.0 0.0 0.0 0
97 0.0 0.0 1.0 1.0 0
98 1.0 0.0 0.0 0.0 0
99 0.0 1.0 0.0 0.0 0

[100 rows x 5 columns]
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>def random_forest(new_point, data) -&gt; 0
</code></pre>

Build a random forest model from scratch.

Random Forest from Scratch

Three zebras are chilling in the desert. Suddenly a lion attacks.
Each zebra is sitting on a corner of a triangle with sides of equal length.
Each zebra randomly picks a direction and only runs along the outline of the triangle to either edge of the triangle.

What is the probability that none of the zebras collide?

Three zebras are chilling in the desert. Suddenly a lion attacks.

Each zebra is sitting on a corner of a triangle with side of equal length. Each zebra randomly picks a direction and only runs along the outline of the triangle to either edge of the triangle.

What is the probability that none of the zebras collide?

Three zebras are chilling in the desert. Suddenly a lion attacks.

Each zebra is sitting on a corner of a triangle with side of equal length.  Each zebra randomly picks a direction and only runs along the outline of the triangle to either edge of the triangle.

What is the probability that none of the zebras collide?



Three Zebras

Challenge

Solidigm Salaries by Position

Company DescriptionJoin a multibillion-dollar global company that brings together amazing technology, people, and operational scale to become a powerhouse in the memory industry. Headquartered in Rancho Cordova, California, Solidigm combines elements of an established, successful technology company with the spirit, agility, and entrepreneurial mindset of a start-up. In addition to the U.S. headquarters and other facilities in the U.S., the company has international presence in Asia, Europe, and the Americas. Solidigm will continue to lead the world in innovating new Memory technologies with aspirations to be the #1 NAND memory company in the world. At Solidigm, we view problems as opportunities to define innovative solutions that hold the power to change the world and unleash the potential technological needs that the future holds. At Solidigm, we are One Team that fosters a diverse, equitable, and inclusive culture that embraces individual uniqueness and empowers us to bring our best selves to deliver excellence in support of Solidigm's vision and mission to be the go-to partner for optimized data storage solutions. You can be part of the takeoff of an innovative business that develops cutting-edge products, delivers strong business value for customers, provides an engaging workplace for its employees, and serves a greater impact on the world. This is a golden opportunity for the right applicant to join us and help design, build, and lead Solidigm. We want a diverse team of dedicated professionals who will not just be Solidigm team members but contribute to how we shape the future of the organization. We are seeking applicants who will grow and thrive in our culture; be customer inspired, trusting, innovative, team-oriented, inclusive, results driven, collaborative, passionate, and flexible. Job DescriptionThe HR Data Analyst plays a vital support role in advancing data-informed decision-making and ensuring People Analytics initiatives align with organizational objectives. This role partners with HR Business Partners and business leaders to deliver impactful workforce insights using tools like Workday Dashboards and Power BI. In addition to contributing to structured analytics processes, the HR Data Analyst will handle ad hoc data and reporting requests to address immediate business needs and provide tailored insights. By assisting in gathering, documenting, and prioritizing analytics requirements, this role translates complex people data into actionable trends, forecasts, and strategic insights. It empowers stakeholders with evidence-based decisions that drive workforce effectiveness and organizational performance while offering the opportunity to build foundational expertise and technical skills in People Analytics . Key Responsibiliti<ul><li>esCollaborate with HR Business Partners and teams to design, develop, and maintain dashboards, reports, and scorecards using tools such as Workday Dashboards, Power BI, and Exce</li><li>l.Conduct workforce data analysis to identify trends and insights related to retention, engagement, productivity, and other HR metric</li><li>s.Support the intake, documentation, and prioritization of People Analytics requirements, ensuring deliverables meet stakeholder need</li><li>s.Respond to ad hoc data and reporting requests from HR and business teams, delivering actionable insights tailored to specific challenge</li><li>s.Optimize workflows for repeatable reporting processes and contribute to automating manual data tasks where possibl</li><li>e.Monitor, validate, and maintain data quality across HR systems, collaborating with HRIS, HRIT, IT, and data stakeholders to uphold data integrit</li><li>y.Support workforce analytics knowledge-sharing and training initiatives to raise data literacy across HR and business team</li><li>s.Support change management and training efforts related to analytics capabilities, tools, and data literacy across the organizatio</li><li>n.Assist in the planning, testing (UAT), and execution of People Analytics initiatives, including go-live readiness and post-implementation review</li><li>s.Stay current on trends in People Analytics, proactively bringing forward ideas to improve user experience and insight deliver</li></ul>y.Note: Responsibilities may change over time and may be adjusted to meet business need s. QualificationsEducati<ul><li>on:Bachelor's degree in Data Analytics, Human Resources, Business Administration, Information Systems, or a related field requi</li><li>redCoursework or certifications in People Analytics, Data Visualization, or Business Intelligence (preferre</li><li>d).An equivalent combination of education and experience may also be conside</li></ul>redExperien<ul><li>ce:3+ years of experience in data analysis, HR analytics, or a related fi</li><li>eldExperience with reporting and analytics tools such as Workday Dashboards, Power BI, or other business intelligence platfo</li><li>rmsExperience in HR metrics, data structures, processes, and key metrics across the employee lifecycle (e.g., talent acquisition, engagement, attrition, performan</li><li>ce)Understanding of core HR processes and data domains (e.g., headcount, turnover, diversity, performance, compensati</li><li>on)Experience gathering and documenting People Analytics requirements initiatives (Workday preferr</li><li>ed)Experience working with HRIS platforms (e.g., Workday, SmartRecruiter) and analytics tools (e.g., Workday Dashboards, Power BI) for data management and analys</li><li>is.Experience in exporting Workday HCM and HRIS data into external systems (e.g., Data Lake, Power B</li><li>I).Experience in Power BI designing interactive dashboards and deriving sophisticated insights using DAX, Power Query, and data modeli</li><li>ng.Exposure to ServiceNow Dashboards is a plus (not require</li><li>d).Experience in a global or multinational environme</li></ul>nt.Skills and Competenci<ul><li>es:Proficient in data analysis and storytelling skills using Workday Dashboards, Power BI, Excel, or other BI/analytics too</li><li>ls.Skilled at interpreting and visualizing complex HR data into clear, actionable insights for diverse audienc</li><li>es.Skilled in gathering and documenting People Analytics requiremen</li><li>ts.Advanced in Excel, including the use of functions, pivot tables, and formulas for data analysis (experience with macros is a plu</li><li>s).Familiarity with AI-driven analytics tools and emerging technologies in HR systems is preferr</li><li>ed.Leverage AI and machine learning tools to enhance people insights and strategic workforce planni</li><li>ng.High attention to detail with the ability to manage and maintain large datasets, ensuring accuracy and consistency across syste</li><li>ms.Strong written and verbal communication skills, with the ability to present data recommendations clearly to various stakeholde</li><li>rs.Ability to handle sensitive data and information with a high degree of discretion and confidentiali</li><li>ty.Understanding of data privacy, governance, and compliance as it relates to HR da</li><li>ta.Self-motivated, with a proactive approach to identifying process improvements and scaling compensation operatio</li><li>ns.Strong customer service orientation, with a commitment to delivering high-quality analytics and solutions to internal stakeholde</li></ul>rs.Language Requiremen<ul><li>ts:Proficiency in English (both written and verbal) is requir</li></ul>ed.Additional Requiremen<ul><li>ts:Adherence to ethical standards and confidentiality in handling sensitive candidate and company informati</li><li>on.Commitment to staying updated with industry trends and best practices to continually improve recruitment processes and outcom</li></ul>es.Working Conditi<ul><li>onsThis role is on-site, based in Guadalajara, with potential for hybrid work arrangemen</li><li>ts.Occasional travel may be required to other regional offic</li></ul> es.

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

Solidigm Interview Questions

Solidigm Interview Guides