Interview preparation roadmap · Technology
Image models and tasks, deep learning, and the coding and deployment questions vision roles ask.
0 / 19 complete · 12 core · 7 other · 104 subtopics
0%Start here
- 01Interview foundations
- 02Coding
- 03ML foundations
- 04Computer vision
- 05Practice
- 06Company & final prep
CoreCommonly assessedGood to knowRole-dependentAdvancedFor deeper interviewsOptionalNot needed by everyoneCompletedYour progressYou are hereNext up
01Interview foundations
What every interviewer sees first.
01.1Resume
A one-page resume you can defend line by line.
What this covers
- One-page format — Reverse-chronological, clean fonts, no photos or tables that confuse parsers.
- Project bullets — Action + what you built + a number (users, speed, accuracy).
- Skills section — Only skills you can answer questions on for two minutes.
- ATS readability — Export a text-based PDF and check it with a parser.
- Working links — GitHub, LinkedIn and a live project link that open.
- Tailoring — Reorder bullets to match the job description.
What to practise
- Run your PDF through a parser test
- Rewrite each bullet as action + result
- Remove any skill you can't explain for 2 minutes
Common questions
- Walk me through your resume.
- Which project here are you most proud of, and why?
- Why is this skill on your resume?
Free resources
- OpenResume — Free ATS-friendly resume builder and parser test (Free)
- Jake's Resume (Overleaf) — The LaTeX resume template most SDE freshers use (Free)
- Resume Worded — Resume and LinkedIn scoring (Free plan)
- Jobscan — Match your resume against a job description (Free plan)
Free courses
- Professional Resume Writing — Alison (Free course)
- Resume, Networking, and Interview Skills — Fullbridge (Free to audit)
01.2Tell me about yourself
A 60–90 second answer: present, past, why this role.
What this covers
- Present — Who you are now: degree, year, focus.
- Past — One or two proof points: a project, internship or result.
- Future — Why this role and this company next.
- Length — 60–90 seconds; stop before you list everything.
- Delivery — Calm pace and eye contact, not a memorised script.
What to practise
- Write it, then say it out loud 5 times
- Record yourself and cut anything over 90 seconds
Common questions
- Tell me about yourself.
- Why should we hire you?
- Where do you see yourself in a few years?
Free resources
- Google Interview Warmup — Free spoken mock interview that prints your transcript (Free)
- BBC Learning English — Structured free English lessons and podcasts (Free)
Free courses
- English@Work: Basic Job Interview Skills — The Hong Kong Polytechnic University (Free to audit)
- Job Interview Preparation For Tech Professionals — University of Cape Town (Free to audit)
01.3Behavioral & STAR
Stories about teamwork, conflict and failure, told with STAR.
What this covers
- STAR structure — Situation, task, action, result. Spend most of the time on action.
- Story bank — 6–8 real stories you can reuse across questions.
- Teamwork & conflict — Disagreeing respectfully and what you changed.
- Failure & weakness — A real one, what you learned, what you do now.
- Ownership — Times you took initiative without being asked.
- Common HR questions — Why us, why you, strengths, relocation.
What to practise
- Write 6 stories in STAR form
- Map each story to 2–3 common questions
- Practise one story with a friend
Common questions
- Tell me about a time you failed.
- Describe a conflict in a team and what you did.
- What is your biggest weakness?
Free resources
- Tech Interview Handbook — Free guide to coding and behavioral interviews (Free)
- Google Interview Warmup — Free spoken mock interview that prints your transcript (Free)
- Pramp / Exponent peer mocks — Free peer-to-peer mock interviews (Free plan)
Free courses
- Master Behavioral Interviews (for Software Engineers) — freeCodeCamp (Free video)
- Behavioral Interview Practice for Computer Science Students — CodeSignal (Free to audit)
- Soft Skills For Tech Professionals — University of Cape Town (Free to audit)
02Coding
Python, sometimes C++.
02.1Programming fundamentals
One language you know well: syntax, memory, complexity.
What this covers
- One language deeply — C++, Java or Python: syntax, standard library, quirks.
- Time & space complexity — Big-O of loops, recursion and common operations.
- Recursion — Base cases, the call stack, when it overflows.
- Memory basics — Stack vs heap, references vs values, garbage collection.
- Strings & arrays — Immutability, copying and slicing costs in your language.
- Debugging — Reading errors, dry runs, using a debugger.
What to practise
- Solve 10 easy problems without an IDE's help
- Explain the complexity of each solution aloud
Common questions
- What is the time complexity of your solution?
- What happens in memory when you call a function?
- Difference between an array and a linked list?
Free resources
- Harvard CS50x — The best first CS course there is (Free)
- HackerRank — Where many campus tests actually run (Free)
- GeeksforGeeks — Company-wise questions and theory (Free plan)
Free courses
- CS50's Introduction to Programming with Python (CS50P) 2022 — Harvard University (Free video)
- MIT 6.100L Introduction to CS and Programming using Python, Fall 2022 — Massachusetts Institute of Technology (Free video)
- Programming in Modern C++ — IIT Kharagpur (Free course)
02.2Core Python
Python language questions for Python roles.
What this covers
- Data types — Mutability, lists vs tuples, dicts and sets.
- Functions — args, kwargs, closures, decorators.
- Iterators & generators — yield and lazy evaluation.
- OOP in Python — Classes, dunder methods, inheritance.
- Error handling — Exceptions and context managers.
- Performance — The GIL, list comprehensions, profiling.
What to practise
- Write a decorator that times a function
- Solve 15 problems in idiomatic Python
Common questions
- What is a decorator?
- Difference between a list and a tuple?
- What is the GIL?
Free resources
- Harvard CS50x — The best first CS course there is (Free)
- Exercism — Practice exercises with human mentoring (Free)
- Kaggle Learn — Short practical courses: Python, Pandas, ML, SQL (Free)
Free courses
- CS50's Introduction to Programming with Python (CS50P) 2022 — Harvard University (Free video)
- MIT 6.100L Introduction to CS and Programming using Python, Fall 2022 — Massachusetts Institute of Technology (Free video)
- CS50's Web Programming with Python and JavaScript (CS50W) 2020 — Harvard University (Free video)
02.3Core C++
C++ language questions for C++ and systems roles.
What this covers
- Pointers & references — Differences and pitfalls.
- Memory — Stack vs heap, RAII, smart pointers.
- OOP in C++ — Virtual functions, vtables, constructors.
- STL — Vectors, maps, iterators, algorithms.
- Templates — Generic code basics.
- Undefined behaviour — Common sources and how to avoid them.
What to practise
- Implement a small vector class
- Rewrite raw-pointer code with smart pointers
Common questions
- What is a virtual function?
- Difference between a pointer and a reference?
- What is RAII?
Free resources
- GeeksforGeeks — Company-wise questions and theory (Free plan)
- Exercism — Practice exercises with human mentoring (Free)
- MIT OpenCourseWare — Real MIT course material, free (Free)
Free courses
- C++ Programming (Complete Course) — Gate Smashers (Free video)
- Programming in Modern C++ — IIT Kharagpur (Free course)
- C++ Programming Course - Beginner to Advanced — freeCodeCamp (Free video)
02.4Data structures & algorithms
The core problem-solving topics commonly assessed in software interviews.
What this covers
- Arrays & hashing — Frequency maps, prefix sums, duplicates.
- Two pointers & sliding window — Pairs, subarrays and substrings in O(n).
- Stacks & queues — Brackets, monotonic stacks, BFS queues.
- Linked lists — Reversal, cycle detection, merging.
- Trees & BST — Traversals, height, LCA, validation.
- Graphs — BFS, DFS, topological sort, shortest paths.
- Binary search — On sorted arrays and on the answer.
- Dynamic programming — 1D and 2D DP, from climbing stairs to knapsack.
What to practise
- Follow one list end to end, not five
- Say the brute force first, then improve it
- One timed problem a day
Common questions
- What is the difference between BFS and DFS?
- How does a hash map handle collisions?
- Find the first non-repeating character in a string.
Free resources
- NeetCode 150 — A curated ordered list instead of 3000 random problems (Free)
- Striver's A2Z DSA sheet — India's most-followed DSA sheet (Free)
- LeetCode — The default interview practice set (Free plan)
- GeeksforGeeks — Company-wise questions and theory (Free plan)
Free courses
- Data Structures and Algorithms Design — IIT Kanpur (Free course)
- MIT 6.006 Introduction to Algorithms, Spring 2020 — Massachusetts Institute of Technology (Free video)
- Neetcode 150 Course - All Coding Interview Questions Solved — freeCodeCamp (Free video)
03ML foundations
The base.
03.1Machine learning fundamentals
Core ML concepts asked in data scientist and ML interviews.
What this covers
- Supervised learning — Regression, classification, common algorithms.
- Bias–variance — Underfitting, overfitting, regularisation.
- Evaluation — Train/test splits, cross-validation, precision, recall, ROC.
- Feature engineering — Encoding, scaling, leakage.
- Trees & ensembles — Random forests and gradient boosting.
- Unsupervised learning — Clustering and dimensionality reduction.
What to practise
- Train and evaluate a model on a public dataset
- Explain precision vs recall with your own example
Common questions
- What is overfitting and how do you prevent it?
- When would you use precision over recall?
- How does a random forest work?
Free resources
- Machine Learning Crash Course — Google's free introduction to machine learning (Free)
- StatQuest — Statistics and ML explained clearly, in videos (Free)
- Kaggle Learn — Short practical courses: Python, Pandas, ML, SQL (Free)
- Machine Learning Interviews book — Free book on ML interview questions and process (Free)
Free courses
- Stanford CS229: Machine Learning led by Andrew Ng | Autumn 2018 — Stanford University (Free video)
- Stanford CS229: Machine Learning Course | Summer 2019 (Anand Avati) — Stanford University (Free video)
- Stanford CS229: Machine Learning I Spring 2022 — Stanford University (Free video)
03.2Deep learning
Neural networks, training and modern architectures.
What this covers
- Neural network basics — Layers, activations, backpropagation.
- Training — Loss functions, optimisers, learning rates.
- Regularisation — Dropout, batch norm, data augmentation.
- CNNs — Convolutions for images.
- Transformers — Attention and why it works.
- Frameworks — PyTorch or TensorFlow basics.
What to practise
- Train a small CNN and explain its errors
- Implement a tiny neural net from scratch
Common questions
- What is backpropagation?
- Why do we need activation functions?
- What is attention?
Free resources
- fast.ai — Practical deep learning, top-down (Free)
- Andrej Karpathy — Zero to Hero — Build a neural net and a GPT from scratch (Free)
- Hugging Face Learn — NLP, LLM, agents and diffusion courses (Free)
- DeepLearning.AI short courses — Short, focused LLM and agent courses (Free plan)
Free courses
- Stanford CS224N: Natural Language Processing with Deep Learning | Winter 2021 — Stanford University (Free video)
- Stanford CS224N: Natural Language Processing with Deep Learning Course | Winter 2019 — Stanford University (Free video)
- MIT 6.7960 Deep Learning, Fall 2024 — Massachusetts Institute of Technology (Free video)
03.3Statistics & probability
The statistics data interviews commonly test, explained in plain words.
What this covers
- Descriptive statistics — Mean, median, variance, percentiles.
- Probability — Conditional probability and Bayes' rule.
- Distributions — Normal, binomial, Poisson and when they apply.
- Sampling & CLT — Why sample means behave predictably.
- Hypothesis testing — p-values, confidence intervals, errors.
- Correlation vs causation — Confounders and how to argue carefully.
What to practise
- Explain a p-value to a non-technical friend
- Solve 20 probability questions
Common questions
- What is a p-value?
- Difference between correlation and causation?
- When would you use the median instead of the mean?
Free resources
- Khan Academy: Statistics and probability — Free statistics course with practice (Free)
- StatQuest — Statistics and ML explained clearly, in videos (Free)
- Khan Academy — Maths and science from scratch (Free)
Free courses
- Descriptive statistics | Probability and Statistics | Khan Academy — Khan Academy (Free video)
- Inferential statistics | Probability and Statistics | Khan Academy — Khan Academy (Free video)
- Random variables and probability distributions | Probability and Statistics | Khan Academy — Khan Academy (Free video)
04Computer vision
The main rounds.
04.1Computer vision
Image models, tasks and data pipelines for vision roles.
What this covers
- Image basics — Pixels, channels, preprocessing.
- CNNs — Convolutions, pooling, architectures.
- Tasks — Classification, detection, segmentation.
- Augmentation — Making models robust.
- Evaluation — IoU, mAP, confusion matrices.
What to practise
- Train an image classifier and analyse failures
- Run an object detector on your own photos
Common questions
- What is IoU?
- Why use data augmentation?
Free resources
- fast.ai — Practical deep learning, top-down (Free)
- Hugging Face Learn — NLP, LLM, agents and diffusion courses (Free)
- Kaggle Learn — Short practical courses: Python, Pandas, ML, SQL (Free)
Free courses
- Stanford CS231N Deep Learning for Computer Vision I 2025 — Stanford University (Free video)
- Computer Vision — IIT Kharagpur (Free course)
- Introduction to computer vision concepts — Microsoft (Free course)
04.2MLOps
Shipping and running ML models reliably.
What this covers
- Experiment tracking — Reproducible runs and versioned data.
- Pipelines — Training and evaluation automation.
- Model serving — APIs, batch jobs, scaling.
- Monitoring — Data drift and model decay.
- CI/CD for ML — Testing data and models.
What to practise
- Serve a model behind an API in a container
- Add drift monitoring to a toy model
Common questions
- How do you know a model in production is degrading?
- How would you version datasets?
Free resources
- DeepLearning.AI short courses — Short, focused LLM and agent courses (Free plan)
- Docker getting started — Official step-by-step guide to containers (Free)
- Machine Learning Interviews book — Free book on ML interview questions and process (Free)
Free courses
- Introduction to machine learning operations (MLOps) — Microsoft (Free course)
- Operationalize machine learning models (MLOps) — Microsoft (Free course)
- MLOps Course – Build Machine Learning Production Grade Projects — freeCodeCamp (Free video)
04.3Projects & resume defense
Explaining what you built, why, and what you would change.
What this covers
- The problem — What it solves and for whom.
- Your role — Exactly what you built yourself.
- Architecture — A simple diagram: frontend, backend, database.
- Hardest bug — What broke and how you found it.
- Trade-offs — Why this stack; what you would change now.
- Scale questions — What happens with 10x the users.
What to practise
- Prepare a 2-minute walkthrough per project
- Be ready to draw its architecture
Common questions
- What was the hardest part of this project?
- Why did you choose this tech stack?
- How would it handle 10x more users?
Free resources
- Tech Interview Handbook — Free guide to coding and behavioral interviews (Free)
- Pramp / Exponent peer mocks — Free peer-to-peer mock interviews (Free plan)
Free courses
- Software Engineering Concepts — MIT OpenCourseWare (Free course)
- Build community-driven software projects on GitHub — Microsoft (Free course)
05Practice
Saying it out loud is a different skill.
05.1Technical mock interviews
At least two full coding mocks before the real one.
What this covers
- Thinking aloud — Narrate your approach before and while coding.
- Clarifying questions — Confirm inputs, scale and edge cases first.
- Handling hints — Take them gracefully and adapt.
- "I don't know" — Say what you do know and how you would find out.
- Review the recording — Fillers, pace, unclear explanations.
What to practise
- Do two peer mock interviews
- Record one spoken mock and listen back
Common questions
- Walk me through your approach before you code.
- How would you test this?
Free resources
- Pramp / Exponent peer mocks — Free peer-to-peer mock interviews (Free plan)
- Google Interview Warmup — Free spoken mock interview that prints your transcript (Free)
- Tech Interview Handbook — Free guide to coding and behavioral interviews (Free)
Free courses
- Software Engineering Job Interview – Full Mock Interview — freeCodeCamp (Free video)
05.2Technical communication
Explaining your thinking clearly in English.
What this covers
- Structure — Big picture first, then details.
- Plain words — Explain without jargon.
- Checking in — "Does that make sense so far?"
- Written English — Clear emails and follow-ups.
What to practise
- Explain one project to a non-CS friend
- Record a 2-minute explanation of your best project and listen back
Common questions
- Explain recursion to a 10-year-old.
Free resources
- BBC Learning English — Structured free English lessons and podcasts (Free)
- LanguageTool — Grammar checking that isn't Grammarly (Free plan)
Free courses
- Technical Communications for Engineers — IIT Roorkee (Free course)
06Company & final prep
The last 48 hours.
06.1Company research
The product, the role, the job description and recent news.
What this covers
- The business — What it sells and to whom.
- The role — What the job description really asks for.
- Recent news — Launches, results, announcements.
- Interview format — The rounds, if the company shares them.
- Your fit — Three reasons, matched to the job description.
What to practise
- Write 3 reasons you want this role
- Match 3 JD lines to your experience
Common questions
- Why do you want to work here?
- What do you know about our product?
Free resources
- GeeksforGeeks — Company-wise questions and theory (Free plan)
- Tech Interview Handbook — Free guide to coding and behavioral interviews (Free)
06.2Questions to ask
Two or three good questions for the end of the interview.
What this covers
- About the team — How work is planned and reviewed.
- About growth — Mentoring, learning, the first 90 days.
- About the product — What is hard right now.
- What to avoid — Questions a quick search answers.
What to practise
- Write 5 questions, keep the best 3
- Prepare one question about the team's own product
Common questions
- Do you have any questions for us?
Free resources
- Tech Interview Handbook — Free guide to coding and behavioral interviews (Free)
06.3Final checklist
Logistics, documents, setup and a calm night before.
What this covers
- Logistics — Time, timezone, link or address.
- Documents — Resume copies, ID, certificates if asked.
- Tech check — Camera, mic, internet, charger.
- Night before — Sleep, light revision, no new topics.
What to practise
- Do a 5-minute tech check the day before
- Lay out documents and clothes the night before
Free resources
- Google Interview Warmup — Free spoken mock interview that prints your transcript (Free)
06.4Job & interview safety
Spot fake offers, payment requests and fake interview links.
What this covers
- Payment requests — Real employers don't charge for jobs or training.
- Email domains — Check it matches the company's own site.
- OTPs & logins — Never share them with anyone.
- Fake interview links — Care with unknown chat apps and downloads.
- Reporting — The 1930 helpline and cybercrime.gov.in.
What to practise
- Check one recent offer against these rules
- Confirm the opening is listed on the company's own careers page
Free resources
- National Cyber Crime Portal — Report online fraud and job scams (Free)
- Have I Been Pwned — Check if your email appears in a data breach (Free)