Adobe's national student build challenge: every team member first clears a DSA-and-programming assessment and a timed product case, then shortlisted teams develop, prototype, and pitch an AI-led product to Adobe leadership.
Online through the prototype stage; grand finale at Adobe Noida
Prize / outcome
MacBook prizes, certificates, finalist visit, and conditional PPI/internship consideration
Team size
2–3 students from the same college
Application fee
Free
Rounds
5
What it is
Adobe University Hackathon is a same-college team competition for engineering students across branches and years. The 2026 theme, "Speak to Agents: The new language of Brand Visibility," sits at the intersection of AI agents, customer experience, and product engineering. Unlike a one-day coding sprint, it tests several different skills in sequence: individual coding speed, concise case analysis, collaborative software development, product UX, documentation, and a leadership-level pitch. That makes team balance more important than stacking a team with three people who all have the same strength.
Rounds
01. Online assessment
Every team member gets one uninterrupted 60-minute attempt: 15 MCQs on algorithms, DSA, and programming logic in 15 minutes, followed by 2 coding problems in 45 minutes. The organiser states that the team's score is the average of its members' scores, so one unprepared teammate can pull down an otherwise strong team.
02. Brand Visibility case study
Shortlisted participants get one 45-minute sitting to analyse a real-world brand-visibility scenario and answer a set of questions. Adobe evaluates analytical thinking, strategic structure, and whether the response turns observations into actions — this is a writing-and-product-thinking round, not another DSA test.
03. Development round
Shortlisted teams receive the full problem brief, attend a live launch session, and build the requested solution. The submission includes a GitHub repository and whatever supporting documentation the brief requires; architecture choices, reproducibility, and a readable README matter alongside the feature itself.
04. Prototype showcase
Teams turn the development-round concept into a working, interactive prototype. The interface must make the core value understandable through smooth navigation and a demonstrable user journey. Only the team leader submits the final package.
05. Grand finale
The top teams present to Adobe leadership at Adobe's Noida headquarters. Adobe covers travel and accommodation for qualifying finalists, who should expect product, technical, feasibility, and adoption questions after the demo.
How to prepare
Step 01
Build a team that can clear every filter
Treat the format as five different jobs, not one generic hackathon. Because the first score is averaged across team members, everyone needs a coding baseline even if one person later owns design or pitching.
Choose 2–3 people from the same college; aim for complementary strength across DSA, full-stack/ML engineering, product design, and speaking.
Make every member complete at least two 60-minute mocks with a 15-minute MCQ block and two timed coding problems.
Agree before the build round who owns repository hygiene, model/backend work, frontend/UX, research, and the final pitch; pair on critical paths so no deliverable has a single point of failure.
Step 02
Prepare for the coding assessment
The official syllabus is algorithms, DSA, and programming logic. Public Adobe assessments and participant reports repeatedly point to fundamentals plus medium-level graph/DP-style problem solving, but the exact mix changes — practise patterns rather than memorising recalled questions.
Practise reading unfamiliar code and predicting output quickly; 15 MCQs in 15 minutes leaves no room for lengthy derivations.
For coding, rehearse a 45-minute routine: read both prompts, choose the higher-confidence problem, pass a simple correct solution, test boundaries, then optimise or move to the second.
Use your contest language and environment before test day; prepare templates only for standard input, graph traversal, and common helpers you genuinely understand.
Step 03
Use a fixed structure for the 45-minute case
A concise, defensible answer beats a long stream of ideas. Allocate roughly 5 minutes to read and define the objective, 30 minutes to answer, and 10 minutes to check assumptions, metrics, risks, and clarity.
Frame each response as: objective → user/brand problem → evidence or assumption → recommendation → execution → success metric → risk and mitigation.
Learn the difference between brand awareness, search visibility, recommendation share, conversion, retention, and trust; do not use them as interchangeable metrics.
Study how AI assistants discover, cite, rank, and sometimes misrepresent brand information. Be ready to discuss structured content, authoritative sources, measurement, safety, and user consent.
Write in short labelled sections. If the portal recommends a word range, prioritise complete reasoning over padding and leave time to proofread every answer.
Step 04
Ship a judgeable prototype
Later rounds reward a working product story. A narrow end-to-end flow with evidence is safer than a large feature list held together by mock screens.
Translate the brief into one primary user, one painful job, one measurable outcome, and an explicit non-goal before choosing technology.
Create a thin vertical slice first: input → core processing → useful result → feedback or action. Add secondary features only after that path is reliable.
Keep a reproducible repository with setup steps, environment-variable examples, architecture diagram, assumptions, limitations, test instructions, and a short demo video fallback.
Instrument a few meaningful metrics and prepare an evaluation set; for AI features, show failure cases, hallucination controls, privacy boundaries, latency, and cost.
Rehearse a 5–7 minute pitch: problem, insight, live journey, architecture, evidence, rollout, risks, and ask. Every teammate should be able to defend the entire solution.
Representative practice — not leaked questions
Questions to practise
01
Given a directed weighted graph, how would you compute the cheapest valid path under an additional state or constraint?
What to show: Practise shortest-path modelling, deciding what belongs in the state, complexity analysis, and unreachable cases.
02
A sequence can be processed one item at a time or in paid batches that reveal the next k items. Find the minimum total cost.
What to show: Practise turning choices over a prefix into dynamic programming and proving the recurrence.
03
An AI assistant rarely mentions a known brand for high-intent category queries. How would you diagnose whether the issue is content availability, authority, retrieval, model behaviour, or user fit?
What to show: Build a measurable issue tree before proposing features; name the data required for each branch.
04
Design a dashboard that tells a brand how visible and accurately represented it is across AI agents without encouraging manipulation.
What to show: Define the user, data collection method, visibility/accuracy metrics, benchmark, UI journey, and ethical safeguards.
05
Your prototype improves brand mentions but sometimes produces unsupported claims. Would you ship it?
What to show: Discuss evaluation thresholds, citations, abstention, human review, monitoring, rollback, and the trade-off between reach and trust.
06
The judge says your idea is just an API wrapper. What proprietary learning loop or defensible workflow makes it a product?
What to show: Explain unique data, feedback, integration depth, evaluation, switching costs, and why the chosen experience solves a real job.
Who can enter
Currently enrolled engineering students across all years, branches, and specialisations
Teams of 2 or 3 students, with every member from the same college or university
Cross-year and cross-specialisation teams are allowed; cross-college teams are not
Each student may join only one team, and every member must complete the individual assessment stages
The competition is free; PPI and internship eligibility can be narrower than hackathon eligibility (the 2026 PPI track is for the 2028 graduating batch)
What you actually get
For the 2026 edition, each winning-team member receives a MacBook Pro and each runner-up-team member a MacBook Neo. National finalists receive Adobe merchandise and a sponsored visit to Adobe's Noida office. The top 50 participants may be considered for pre-placement interviews and an Adobe internship; that hiring benefit is restricted to the eligible graduating batch stated for the edition (2028 for the 2026 event), so it should not be read as an automatic internship for every finalist.
Frequently asked questions
What is asked in the Adobe University Hackathon online assessment?+−
For 2026, every participant gets 60 minutes for 15 MCQs on algorithms, DSA, and programming logic plus 2 coding problems. The team score is calculated from its members' performance, so the assessment cannot be delegated to the strongest coder.
What happens in Adobe's Brand Visibility case-study round?+−
Shortlisted participants analyse a real-world brand-visibility scenario and answer several questions in one 45-minute sitting. The stated evaluation areas are analytical thinking, strategic approach, and actionable insight. Use a repeated objective–analysis–recommendation–metric–risk structure so every answer is complete.
What coding topics should I prepare?+−
Start with complexity and programming logic, then arrays/strings, hashing, sorting/searching, stacks/queues, trees, graphs, greedy methods, and dynamic programming. Public experiences can suggest patterns, but they are not a syllabus and exact questions change; practise timed mixed sets rather than memorising recalled prompts.
Do all Adobe hackathon team members take the assessment?+−
Yes. Every member receives one attempt, and the organiser says the team result uses the average of member scores. Pick teammates who will all prepare for Round 1, even if their main contribution later is product, design, or pitching.
What should the development-round GitHub repository contain?+−
At minimum: working code, exact setup steps, an example environment file without secrets, architecture and data-flow notes, assumptions, known limitations, test instructions, and a clear mapping from the problem statement to implemented features. Follow the edition-specific submission checklist when Adobe releases it.
How polished must the prototype be?+−
It should be reliable enough to demonstrate the core user journey interactively. Prioritise one complete flow, legible UX, realistic data, and measurable value over many unfinished screens. Keep a recorded demo as a fallback, but prepare to run the product live.
Can first-year or non-CS students enter?+−
Yes. The 2026 rules allow engineering students from every year, branch, and specialisation. Teams may mix years and specialisations, but every member must be from the same college or university.
Does reaching the finale guarantee an Adobe internship?+−
No. The 2026 edition says the top 50 participants may receive PPI and internship consideration, subject to eligibility; only the 2028 graduating batch is eligible for that edition's internship PPI. Finale prizes and certificates are separate from hiring.
Format and eligibility checked against Adobe's official University Hackathon page and the organiser's 2026 Unstop brief, 15 Aug 2026. Preparation themes were cross-checked against public Adobe assessment experiences on GeeksforGeeks and LeetCode; the practice prompts above are original representative exercises, not leaked or guaranteed questions. Dates, theme, rewards, and hiring eligibility change by edition.