Customer Obsession
Q: Tell me about a time you solved a pain point for customers.
Situation: At the Hack for Social Impact, the UN representative outlined a critical challenge:
decision-makers struggled to access and correlate data on land degradation, drought, and
socioeconomic factors. This lack of integrated, actionable insights hindered effective
policy-making and resource allocation.
Task: My team was tasked with designing a tool that could simplify complex data retrieval and
analysis to empower informed decisions for environmental and humanitarian efforts.
Action: We developed LandDrop, an advanced analytical tool powered by Fetch.ai agents and
Azure-hosted LLMs. I played a key role in creating features that allowed users to drop a pin on
the map to instantly retrieve data on variables such as soil moisture, poverty levels, and
migration patterns. I also integrated dynamic graphs to visualize correlations and an AI assistant
to provide plain-language insights for complex queries, making the tool both powerful and
user-friendly.
Result: The UN representative commended LandDrop for its innovative design and usability,
highlighting its potential to drive impactful policy changes. The project won first place in the
Fetch.ai track, and its success underscored the tool's ability to address real-world challenges at
a global scale.
Leadership Principle: Ownership
Q: Tell me about a time when you stepped into a leadership role in a challenging
situation.
Situation: During my internship at Car-rillo, we encountered a major challenge with the
database system, PocketBase. It couldn’t handle the increasing number of user requests,
leading to frequent slowdowns. There was no designated owner for addressing the issue, and
deadlines for other tasks made it difficult for the team to prioritize this.
Task: I recognized the importance of resolving this issue to improve app performance and took
the initiative to lead the migration to a more scalable solution.
Action: I began by analyzing the existing database schema and proposed migrating to MySQL,
a free and more scalable option. I developed a detailed plan to handle the migration, including
data backups, schema optimization, and extensive testing to minimize risks. I coordinated with
teammates to test the new setup thoroughly and scheduled the migration during off-peak hours
to avoid disrupting active users.
Result: The migration was completed smoothly, reducing data retrieval times by 22% and
significantly enhancing app responsiveness. My initiative not only solved a critical issue but also
earned recognition from the CEO and team for stepping up to lead in a challenging situation.
This experience reinforced my ability to take ownership and deliver results under pressure.
Invent & Simplify
Q: Tell me about a time you improved a complex process.
Situation: While interning at the SJSU College of Engineering, one of the most reported issues
was slow and inefficient PDF processing, particularly for large files.
Task: My goal was to improve the PDF processing system to enhance performance and user
satisfaction.
Action: I migrated the encoding convention from Base64 to FormData within an Express.js
framework, optimizing the handling of large, high-resolution files. I also developed a feature to
preview PDFs directly in the browser.
Result: This solution increased PDF processing speed by 20% and significantly reduced bug
reports. The changes were praised by the IT department and led to a measurable improvement
in user satisfaction, reflected in post-deployment feedback surveys.
Are Right, A Lot
Q: Tell me about a time when you had to make a decision without much customer data.
Situation: At an early-stage startup, Car-rillo, we were tasked with building a responsive CRM
mobile interface for car dealerships. Limited customer feedback and unclear requirements made
the design process challenging.
Task: I needed to design an intuitive interface that would address the primary needs of car
dealership employees.
Action: I conducted interviews with a few dealership managers to infer user pain points and
focused on designing a minimalist interface with React Native. I incorporated flexible filtering
and sorting options to address common use cases.
Result: The new interface received positive feedback from dealership staff, citing its simplicity
and speed. The CRM adoption rate among early users increased by 25%.
Learn & Be Curious
Q: Tell me about a skill you recently learned.
Situation: For a project at Cal Hacks, I needed to build an AI-powered database assistant
capable of processing natural language queries.
Task: My challenge was to learn Fetch.ai's agent-based technology to develop a multi-agent
network for the assistant.
Action: I studied Fetch.ai's documentation and collaborated with teammates to integrate their
tools. I also explored Groq LLM models for query validation and analysis. This effort required
learning advanced concepts around tool workflows and natural language parsing.
Result: The project, Databae, won Fetch.ai’s track at Cal Hacks 11.0. It showcased innovative
use of agent-based technology, significantly improving data query handling and insights
generation for users.
Here are STAR-based stories tailored to other Amazon Leadership Principles, focusing on
impact and measurable outcomes:
Invent & Simplify
Q: Tell me about a time you solved a complex problem.
Situation: During my internship at Hiair.ai, our AI interview bot struggled to generate consistent
and job-specific feedback due to the large variance in user queries. This inconsistency created
frustration among users during beta testing.
Task: I was tasked with improving the bot’s ability to provide accurate and tailored responses
while maintaining scalability for diverse use cases.
Action: I implemented a Retrieval-Augmented Generation (RAG) model that integrated a
custom database of job-specific information. This approach improved response generation by
grounding the bot's answers in verified data. Additionally, I streamlined the query handling
workflow by fine-tuning intent recognition models to better assess user inputs.
Result: The enhancements increased user satisfaction scores by 40% during testing and
reduced feedback error rates by 25%. The bot became a standout feature, praised by users for
its practical and personalized guidance.
Bias for Action
Q: Tell m111123123e about a tough decision you made during a project.
Kmkmkpasdfsdfdfgadfgdsafgasd her testing or deploy the current build, knowing it might still
have minor bugs.
Action: I conducted a quick risk assessment with the team and prioritized fixing critical bugs
while documenting the minor ones for a post-release patch. This decision allowed us to deploy
on time, ensuring students could use the printer system during finals week.
Result: The release was a success, with no major issues reported. Post-deployment patches
resolved remaining bugs within two days, and the system received positive feedback for its
improved efficiency, processing files 20% faster than before.
Dive Deep
Q: Tell me about a time when you had to dig deep to discover the root cause of a
problem.
Situation: At Car-rillo, a CRM mobile app for car dealerships, clients reported occasional
slowdowns when syncing data between the app and the server. The issue was intermittent,
making it difficult to diagnose.
Task: My task was to identify the root cause of the slowdowns and implement a solution to
ensure seamless performance.
Action: I analyzed server logs, network traffic, and database queries. After thorough testing, I
identified a poorly optimized query in the app's backend that caused bottlenecks during high
traffic. I refactored the query and implemented caching for frequently accessed data to reduce
server load.
Result: The fix reduced data retrieval times by 22%, and the app's performance metrics
improved significantly. Dealership staff reported fewer complaints, and user engagement
increased by 15% in the following month.
Here are STAR-based stories tailored to other Amazon Leadership Principles, focusing on
impact and measurable outcomes:
Invent & Simplify
Q: Tell me about a time you solved a complex problem.
Situation: During my internship at Hiair.ai, our AI interview bot struggled to generate consistent
and job-specific feedback due to the large variance in user queries. This inconsistency created
frustration among users during beta testing.
Task: I was tasked with improving the bot’s ability to provide accurate and tailored responses
while maintaining scalability for diverse use cases.
Action: I implemented a Retrieval-Augmented Generation (RAG) model that integrated a
custom database of job-specific information. This approach improved response generation by
grounding the bot's answers in verified data. Additionally, I streamlined the query handling
workflow by fine-tuning intent recognition models to better assess user inputs.
Result: The enhancements increased user satisfaction scores by 40% during testing and
reduced feedback error rates by 25%. The bot became a standout feature, praised by users for
its practical and personalized guidance.
Bias for Action
Q: Tell me about a tough decision you made during a project.
Situation: At the SJSU College of Engineering, a project to optimize PDF printing encountered
a critical deadline conflict. With only 24 hours remaining before deployment, we realized our
solution could either prioritize speed or stability, but not both.
Task: I had to decide whether to delay the release for further testing or deploy the current build,
knowing it might still have minor bugs.
Action: I conducted a quick risk assessment with the team and prioritized fixing critical bugs
while documenting the minor ones for a post-release patch. This decision allowed us to deploy
on time, ensuring students could use the printer system during finals week.
Result: The release was a success, with no major issues reported. Post-deployment patches
resolved remaining bugs within two days, and the system received positive feedback for its
improved efficiency, processing files 20% faster than before.
Dive Deep
Q: Tell me about a time when you had to dig deep to discover the root cause of a
problem.
Situation: At Car-rillo, a CRM mobile app for car dealerships, users reported occasional
slowdowns when syncing data between the app and the server. The issue was intermittent,
making it difficult to diagnose.
Task: My task was to identify the root cause of the slowdowns and implement a solution to
ensure seamless performance.
Action: I analyzed server logs, network traffic, and database queries. After thorough testing, I
identified a poorly optimized query in the app's backend that caused bottlenecks during high
traffic. I refactored the query and implemented caching for frequently accessed data to reduce
server load.
Result: The fix reduced data retrieval times by 22%, and the app's performance metrics
improved significantly. Dealership staff reported fewer complaints, and user engagement
increased by 15% in the following month.
Deliver Results
Q: Tell me about a project you worked on with a tight deadline.
Situation: At the SiliconX Hacks hackathon, my team had just 36 hours to build an AI-powered
interview assistant capable of simulating real-world behavioral interviews for students.
Task: As the lead engineer, I needed to ensure the project was functional, polished, and
impactful enough to stand out in a competitive environment.
Action: I divided the workload among team members, focusing on key deliverables: I
implemented voice input/output integration, designed a session-based conversation flow using
Firebase, and created a dynamic front-end interface with React and Tailwind. I worked late into
the night debugging edge cases and testing functionality.
Result: The project, Celia AI, was completed on time and won the Most Impactful for
Students award. It was praised for its realistic interview simulation and its potential to improve
student preparation for job applications.
Think Big
Q: Tell me about a time you came up with an innovative solution to a problem.
Situation: While interning at Car-rillo, an early-stage startup, we were using PocketBase to
manage our database. However, as user traffic increased, the system struggled to handle the
load, and budget constraints ruled out costly database solutions. It became clear that our
current setup was not scalable for the company's growth.
Task: I needed to implement a solution that not only addressed immediate performance issues
but also positioned the system for future scalability while staying within budget.
Action: I proposed and led the migration from PocketBase to MySQL, a scalable, free solution. I
optimized the database schema to reduce redundancy and improved query performance to
handle high-traffic scenarios. To ensure a seamless transition, I scheduled the migration during
off-peak hours and thoroughly tested the system to minimize any potential downtime.
Result: The migration reduced data retrieval times by 22% and significantly improved app
responsiveness. This future-proofed the system for scaling as user traffic grew, positioning the
company to handle projected growth without incurring additional infrastructure costs. The CEO
and engineering team recognized the solution as a critical step in supporting the company’s
long-term vision.
Bias for Action
Q: Describe a situation where you had to act quickly to resolve an issue.
Situation: During my internship at the SJSU College of Engineering, a critical printing bug was
discovered at 11 PM, preventing students from submitting assignments due the next morning.
Task: I needed to debug and resolve the issue before the system's high-usage period began.
Action: I immediately went to campus and traced the problem to a misconfiguration in the
printer routing logic caused by a recent update. I corrected the issue, tested it extensively, and
redeployed the fix overnight.
Result: By the following morning, the system was fully operational, allowing students to submit
their assignments without delays. My proactive approach earned recognition from my manager
for going above and beyond to ensure system reliability.
Q1: Tell me about a time when you were faced with a problem that had a
number of possible solutions. What was the problem, and how did you
determine the course of action? What was the outcome of that choice?
Leadership Principle: Are Right, A Lot
Situation: During my internship at Hiair.ai, we needed to optimize the AI interview bot’s ability to
assess user responses. We had multiple approaches to improve it: implementing more intent
recognition, adding external datasets, or fine-tuning existing LLMs.
Task: I had to evaluate these options and choose the most effective and efficient solution to
enhance the bot's performance.
Action: I conducted a quick cost-benefit analysis. Adding external datasets required significant
time, while intent recognition would directly address user pain points with minimal changes. I ran
a test implementing a refined intent recognition pipeline and benchmarked it against the existing
model.
Result: The intent recognition approach improved response accuracy by 25% and reduced
misclassification errors by 30%. The lightweight implementation fit our timeline and had a
noticeable positive impact on user satisfaction.
Q2: When did you take a risk, make a mistake, or fail? How did you
respond, and how did you grow from that experience?
Leadership Principle: Earn Trust
Situation: During my internship at the SJSU College of Engineering, I introduced a new feature
for handling PDF previews. Unfortunately, I overlooked testing it on Safari, leading to a
production issue where Safari users couldn’t access the preview feature.
Task: I needed to quickly address the issue and ensure it didn’t erode trust in our team’s work.
Action: I acknowledged my oversight to my manager, worked late into the night to debug the
issue, and deployed a hotfix within 24 hours. I also updated our testing pipeline to include
cross-browser compatibility checks for future releases.
Result: The fix restored functionality for Safari users the next morning, and I learned the
importance of thorough testing. This experience helped me mature as an engineer and led to a
process improvement that benefited the entire team.
Q3: Describe a time you took the lead on a project.
Leadership Principle: Ownership
Situation: While interning at Car-rillo, our CRM system faced significant performance issues
due to the limitations of the PocketBase database. As the database grew, user request times
became unacceptably slow, and a migration to a more scalable solution was needed.
Task: I volunteered to lead the database migration project, ensuring minimal disruption to the
app’s users while improving performance and scalability.
Action: I analyzed the existing database structure, designed an optimized schema for MySQL,
and planned the migration to occur during off-peak hours to minimize downtime. I also led the
testing efforts to ensure the new database handled all existing queries efficiently. Additionally, I
coordinated with the engineering team to adjust backend APIs for compatibility with MySQL.
Result: The migration reduced data retrieval times by 22% and resolved the performance
bottlenecks. The seamless transition was completed without any downtime, earning recognition
from both the engineering team and the CEO for demonstrating leadership in tackling a critical
challenge.
Q4: What did you do when you needed to motivate a group of individuals or
promote collaboration on a particular project?
Leadership Principle: Strive to Be Earth’s Best Employer
Situation: During the development of Databae at Cal Hacks, our team hit a roadblock halfway
through the hackathon, with disagreements about prioritizing features. Tensions were high due
to the tight deadline.
Task: I needed to refocus the team, align our efforts, and restore collaboration to finish the
project on time.
Action: I called a quick team meeting to identify everyone’s concerns and proposed breaking
the work into smaller, manageable milestones. I emphasized the importance of delivering a
polished MVP and how we could integrate other features after the hackathon. To motivate the
team, I acknowledged everyone’s contributions and encouraged open communication.
Result: The team regained focus and worked efficiently to complete Databae, which went on to
win Fetch.ai’s track at Cal Hacks. The project was recognized for its innovation and teamwork,
and the experience strengthened my skills in conflict resolution and team collaboration.
hmm tbh this is too broad. like the structure seem decent but it is kinda all over the place it
might give the interviewer a harder time following what you're trying to portray. In my opinion, i
think you should provide more focus on more narrow problems. So right now the problem you
have is
```We struggled with managing large datasets, handling marker rendering bugs in Mapbox, and
dealing with state synchronization issues in React.```
and then all of this to cover all those problems:
```
T: My goal was to step up and lead the team through the technical challenges we were facing. I
needed to help teammates stay unblocked, divide responsibilities clearly, and make sure we
could deliver a stable, high-quality project before the hackathon ended. At the same time, I took
ownership of key technical components myself, contributing directly to the fire risk scoring
system and helping stabilize the map rendering issues, so that I could support the team's
progress both through leadership and by working hands-on alongside them.
A: I organized the project into specific workstreams. One teammate handled cleaning and
merging utility datasets, another focused on frontend visualization with Mapbox, and another
integrated Gemini API prompts for building-level insights. I personally designed the fire risk
scoring system and worked on stabilizing the map rendering logic. When teammates ran into
issues, such as markers not updating after score generation or popups causing React crashes, I
jumped in to debug state flows, restructured re-rendering logic, and simplified dynamic styling to
avoid race conditions. I also set internal checkpoints, regularly checked on teammates'
progress, and reassigned tasks whenever necessary to keep momentum high. Throughout the
process, I made sure we always had a working version and avoided last-minute integration risks
by encouraging fast, small deliverables and steady progress.
```
What I would do is pick a more specific problem during the hack.
Examples could be maybe:
some team conflict --> you took initiative to manage the team and align team efforts etc...
Having to make difficult decision --> risk analysis, etc.
In foresight you saw potential issues --> took initiative to redirect the team etc.
Saw potential drastic improvements that could be made --> took initiative to deliver those results
etc.
