Question 1

Tell me about yourself.

Accepted Answer

Use present, past, future, in about 60 to 90 seconds. Present: "I'm a Java backend developer with four years at a fintech company, where I build payment APIs." Past: "Over the last year I moved towards AI. I built a document question answering app with RAG and an evaluation set, and I used an LLM to classify support tickets at work, which cut manual triage by about 30%." Future: "Now I want to do this full time as an AI engineer, and this role fits because you build LLM features on top of real backend systems, which is exactly where my strengths meet."

Question 2

Why do you want to move into AI, and why this particular role?

Accepted Answer

Give a real reason and connect it to evidence. "In my testing work I kept seeing the same problem: thousands of test cases written and checked by hand. When I tried an LLM to draft test cases, it saved hours, but it also made mistakes I had to catch. That got me interested in building AI systems that are actually reliable. I've since completed projects in RAG and evaluation. I'm applying for this role because it focuses on shipping AI features to users, and my background in quality and testing directly helps with evaluating AI output."

Question 3

Why do you want to work here?

Accepted Answer

Show you've done homework. Mention one thing about their product, one thing about how they work, and link both to you. "I've used your app for expense reports, and I noticed the receipt scanning recently got much better, so I read your engineering blog about it. I like that your team publishes how you evaluate models, because I care about measuring AI quality. And since you serve small businesses in India, my experience building for that market would help from day one."

Question 4

You haven't had an AI job title yet. Why should we hire you over someone who has?

Accepted Answer

Don't be defensive. Turn the gap into a strength. "That's fair. What I bring is production experience: I've spent five years shipping and supporting real systems, handling incidents and working with stakeholders. Many AI projects fail at exactly that stage, after the demo. On the AI side, I've built two end to end projects with evaluation, which I can show you. So you'd get someone who learns fast and already knows how to make software work in production."

Question 5

What's the difference between AI, machine learning, deep learning and generative AI?

Accepted Answer

They are circles inside each other. AI is the broad goal of making computers do things that seem intelligent. Machine learning is one way to do that: instead of writing rules, we let the computer learn patterns from examples. Deep learning is machine learning with many layered neural networks, which works well for images, audio and text. Generative AI uses deep learning models, like LLMs, to create new content such as text, images or code, instead of only predicting a label.

Question 6

Explain supervised, unsupervised and reinforcement learning, with an example of each.

Accepted Answer

Supervised learning learns from examples that have the right answer, called labels. For example, emails marked spam or not spam, so the model learns to predict spam. Unsupervised learning has no labels; it finds structure by itself, like grouping customers into segments by behaviour. Reinforcement learning learns by trial and error with rewards, like a game playing agent or a robot that gets a reward when it does the task well. LLMs also use reinforcement learning from human feedback to make answers more helpful.

Question 7

What is overfitting, and how do you prevent it?

Accepted Answer

Overfitting is when a model memorises the training data, including its noise, so it scores very well on training data but badly on new data. It's like a student who memorises last year's answers but can't solve a new question. You spot it when training accuracy is high and validation accuracy is much lower. To prevent it: get more data, use a simpler model, add regularisation, use cross validation, stop training early, and for neural networks use dropout.

Question 8

What is the bias variance trade off?

Accepted Answer

Bias is error from a model that's too simple, so it misses real patterns. That's underfitting. Variance is error from a model that's too sensitive to the training data, so small changes in the data change it a lot. That's overfitting. Making a model more complex usually lowers bias but raises variance. The goal is the sweet spot where total error on new data is lowest, which we find using validation data. For example, a straight line through curved data has high bias; a very deep decision tree has high variance.

Question 9

Why do we split data into training, validation and test sets?

Accepted Answer

The training set is what the model learns from. The validation set is used while building, to compare models and tune settings like depth or learning rate. The test set is kept locked away until the very end to get an honest estimate of how the model will do on new data. If you tune on the test set, you slowly overfit to it and your final number becomes too optimistic. For time based data, split by time, so you train on the past and test on the future.

Question 10

Explain precision and recall. When would you prefer one over the other?

Accepted Answer

Precision: of everything the model flagged as positive, how many were really positive. Recall: of all the real positives, how many the model caught. For a cancer screening test or fraud detection, missing a real case is very costly, so we prefer high recall, even if we get some false alarms. For a spam filter, putting a real email into spam is annoying, so we prefer high precision. F1 score combines both when you need one number, and you can move between them by changing the decision threshold.

Question 11

Your model is 99% accurate, but the business says it's useless. How is that possible?

Accepted Answer

Probably the classes are imbalanced. If only 1% of transactions are fraud, a model that always says "not fraud" is 99% accurate and catches zero fraud. Accuracy hides that. I'd look at the confusion matrix, precision and recall for the rare class, and metrics like PR AUC. Then I'd check the business goal: maybe they need to catch 80% of fraud while keeping false alarms low enough for the review team to handle, and I'd tune the threshold for that.

Question 12

What is regularisation? What's the difference between L1 and L2?

Accepted Answer

Regularisation adds a penalty for large weights to the loss, which keeps the model simpler and reduces overfitting. L2, also called ridge, penalises the square of weights, so it shrinks all weights smoothly towards zero but rarely to exactly zero. L1, also called lasso, penalises the absolute value, which pushes some weights to exactly zero, so it also does feature selection. In neural networks we also use dropout and weight decay, which is L2 in practice.

Question 13

What's the difference between a random forest and gradient boosting?

Accepted Answer

Both combine many decision trees. A random forest trains lots of deep trees independently on random samples of rows and features, then averages their votes. That reduces variance and is hard to break. Gradient boosting trains small trees one after another, where each new tree fixes the errors of the ones before it. Boosting, like XGBoost or LightGBM, usually gives the best accuracy on tabular data but needs more careful tuning and can overfit if you add too many trees.

Question 14

Explain gradient descent in simple words. What does the learning rate do?

Accepted Answer

Imagine you're on a hill in fog and want to reach the bottom. You feel which way the ground slopes and take a step downhill, again and again. Gradient descent does the same with the model's error: the gradient tells us which direction increases the error, so we move the weights a small step in the opposite direction. The learning rate is the step size. Too big and you overshoot and bounce around; too small and training is very slow. In practice we use mini batches and optimisers like Adam.

Question 15

How does an LLM generate text?

Accepted Answer

An LLM is a neural network, a transformer, trained on huge amounts of text to predict the next token, which is a word or piece of a word. When you send a prompt, it's split into tokens, the model calculates probabilities for every possible next token, picks one, adds it to the text, and repeats until it's done. Settings like temperature control how random that choice is. Because it predicts plausible text rather than looking up facts, it can confidently produce wrong answers, which we call hallucinations.

Question 16

What do temperature and top p control? What would you use for a data extraction task?

Accepted Answer

Both control randomness when choosing the next token. Temperature reshapes the probabilities: low temperature makes the model pick the most likely tokens, so it's more consistent; high temperature makes it more creative and varied. Top p limits choices to the smallest set of tokens whose probabilities add up to p. For extraction or classification I'd use temperature 0 or close to it, plus a fixed output format, because I want the same input to give the same, correct output. For brainstorming I'd raise it.

Question 17

What's the difference between a list, tuple, set and dictionary in Python? When would you use each?

Accepted Answer

A list is an ordered, changeable collection; good for sequences you add to, like rows to process. A tuple is ordered but can't be changed; good for fixed records like coordinates, and it can be a dictionary key. A set holds unique items with very fast membership checks; good for removing duplicates or checking "have I seen this ID?". A dictionary maps keys to values with fast lookup; good for things like user ID to user details. Checking membership in a set or dict is O(1) on average, but O(n) in a list.

Question 18

Explain INNER JOIN vs LEFT JOIN with an example.

Accepted Answer

An INNER JOIN returns only rows that match in both tables. A LEFT JOIN returns every row from the left table, plus matches from the right; where there's no match, the right side columns are NULL. Example: customers and orders. An inner join gives only customers who have ordered. A left join from customers gives all customers, and those with NULL order IDs are the ones who never ordered, which is a common way to find inactive customers.

Question 19

What are window functions? Explain ROW_NUMBER, RANK and DENSE_RANK.

Accepted Answer

Window functions calculate something across related rows without collapsing them like GROUP BY does. For example, a running total or each employee's salary rank within their department, using OVER (PARTITION BY department ORDER BY salary DESC). With ties: ROW_NUMBER gives unique numbers 1, 2, 3 even for ties, so add a tie breaker column to make it deterministic. RANK gives ties the same number and then skips, like 1, 1, 3. DENSE_RANK gives ties the same number without gaps, like 1, 1, 2. LAG and LEAD let you compare a row with the previous or next one.

Question 20

Write a query to get the top 3 products by revenue in each category.

Accepted Answer

I'd rank products within each category and then filter. First, in a subquery or CTE, compute revenue per product and add DENSE_RANK() OVER (PARTITION BY category ORDER BY revenue DESC) AS rnk. Then select rows WHERE rnk <= 3. I'd say out loud how I handle ties: DENSE_RANK keeps all tied products, which could return more than 3 rows; if they want exactly 3, I'd use ROW_NUMBER with a tie breaker like product ID.

Question 21

What's the difference between correlation and causation? How would you prove causation?

Accepted Answer

Correlation means two things move together; causation means one actually causes the other. Ice cream sales and drownings are correlated because both rise in summer, not because ice cream causes drowning. In products, users who use a new feature might retain better simply because they were already more engaged. To show causation, the best tool is a randomised A/B test: randomly give the feature to half the users, so the only systematic difference is the feature, then compare outcomes.

Question 22

How do you approach feature engineering for a new problem?

Accepted Answer

I start from the domain: what would a human expert look at to make this decision? For predicting late loan payments, that's things like income to EMI ratio, past late payments and recent changes in spending. Then I clean and encode: handle missing values, one hot encode categories, scale numbers if the model needs it, and create time based features like "days since last purchase". I check every feature for leakage and measure value with validation scores and feature importance. Usually a few good domain features beat many random ones.

Question 23

How do you handle an imbalanced dataset, like 1% fraud?

Accepted Answer

First, choose the right metric: precision, recall, PR AUC, not accuracy. Then options: use class weights so mistakes on the rare class cost more; resample, either undersample the majority or oversample the minority (SMOTE), only on the training data; and tune the decision threshold to hit the recall or precision the business needs. Also make sure the validation split keeps the same ratio, using a stratified split. Often, getting more examples of the rare class or better features helps most.

Question 24

How do you deal with missing values?

Accepted Answer

First I ask why they're missing, because that changes the fix. If income is missing because people didn't want to share it, the fact that it's missing may itself be a signal. Options: drop rows if very few are missing; fill with the median or most common value; fill using a model or a group average; or add a "was missing" flag column alongside the filled value. Tree models like XGBoost can handle missing values natively. Whatever I do, I fit the filling logic on the training data only.

Question 25

How do you choose which model to use?

Accepted Answer

I start with a simple baseline, like logistic regression or even a rule, so I know what "good" means. For tabular data, I'd then try gradient boosting because it's usually strongest. For images, text or audio, I'd use pretrained deep learning models or an LLM. Then I consider constraints: latency, cost, how much data I have, and whether the result must be explainable, for example in lending. I pick the simplest model that meets the target on validation data, because it's easier to maintain.

Question 26

What steps do you follow in a machine learning project, from start to finish?

Accepted Answer

One, understand the problem and the success metric with the people who need it. Two, collect the data and explore it: size, missing values, distributions and obvious errors. Three, clean the data and create features. Four, split into training, validation and test sets. Five, train a simple baseline, then better models, comparing them on validation data. Six, evaluate the final model once on the test set and check its mistakes. Seven, deploy it, for example as an API or a Streamlit app. Eight, monitor it and retrain when data changes.

Question 27

You get a new dataset. How do you explore it?

Accepted Answer

I start with the shape and column types, then look at the first few rows to understand what each column means. Next, missing values per column, duplicates, and basic statistics like mean, median, minimum and maximum, which reveal impossible values like negative ages. I plot distributions with histograms and count the categories. Then I look at relationships: how each feature relates to the target, and correlations between features. I write down surprises and questions for the data owner, because many problems are understood only by asking.

Question 28

How did you learn AI, and what are you learning right now?

Accepted Answer

Be concrete and honest. "I started with Python and statistics, then did a structured machine learning course and built small projects on Kaggle datasets. The biggest jump came from building end to end projects: a loan default model I deployed on Streamlit, and a PDF question answering app with an LLM API. Right now I'm learning how to evaluate LLM apps properly, because my first version gave wrong answers I didn't notice until I built a test set."

Question 29

How would you build a spam email classifier?

Accepted Answer

Clarify: what counts as spam, and which mistake is worse? Usually marking a real email as spam is worse, so precision matters. Data: emails labelled spam or not. Features: start with text turned into numbers, using TF IDF, plus simple signals like number of links or the sender's history. Model: a baseline with logistic regression or naive Bayes, which works well for text, then compare with a fine tuned small language model if needed. Evaluate with precision and recall on a test set, and pick a threshold that keeps false positives very low. Deploy it as a service, collect "not spam" corrections from users, and retrain regularly because spammers change tactics.

Question 30

How would you build a chatbot that answers students' questions from college notices and PDFs?

Accepted Answer

I'd use RAG. First, collect the notices and PDFs, extract their text, split it into chunks of a few paragraphs, create embeddings and store them in a vector database. When a student asks a question, embed the question, find the most similar chunks, and send them to an LLM with the instruction to answer only from them and to show which notice the answer came from. If nothing relevant is found, it should say so and point to the office. I'd test it with 30 real student questions, fix the failures, deploy it with Streamlit, and add a feedback button.

Question 31

Walk me through a project you're proud of.

Accepted Answer

Use STAR and go deep on your own decisions. "Situation: our support team was overwhelmed with repeated questions. Task: I wanted to build an assistant that answers from our help docs. Action: I built a RAG pipeline, but the first version answered only 60% of test questions correctly. I created an evaluation set of 80 real questions, found that most failures came from chunks splitting tables, switched to structure aware chunking and added reranking. Result: correct answers rose to 86%, and in a pilot it resolved about a quarter of tickets without an agent." Be ready for "why did you choose that?" at every step.

Question 32

Tell me about a time something you built failed.

Accepted Answer

Pick a real failure with a lesson, not a fake one. "I shipped a classification model that looked great offline, with 92% accuracy, but within two weeks users complained. I had split data randomly, and the same customers appeared in both training and test, so the score was too optimistic. I owned it with my manager, re split by customer, got a realistic 81%, and improved features to reach 86%. Since then, I always ask how the data is grouped before splitting, and I add a review of the evaluation setup to my checklist."

Question 33

Tell me about a disagreement with a teammate or stakeholder.

Accepted Answer

Show you disagree with data and respect. "A product manager wanted to launch our AI reply feature to all customers. I was worried because our tests showed it sometimes made up refund policies. Instead of just saying no, I showed five real examples and the error rate from our eval set, and proposed launching to 10% of tickets with agent approval first. We agreed on criteria to expand. Two weeks later the error rate was below our threshold and we expanded. We kept a good relationship because we focused on the shared goal."

Question 34

Explain a technical concept to me as if I'm not technical.

Accepted Answer

Pick something from your work and use an everyday analogy. For example, explaining RAG: "Imagine a very smart new employee who has read a lot but knows nothing about our company. If you ask them a policy question, they might confidently guess. RAG is like giving them the right pages of our policy manual before they answer, and asking them to point to the page. So answers are based on our actual documents, and you can check them." Then check understanding: "Does that make sense?"

Question 35

How do you keep up with the fast changes in AI?

Accepted Answer

Be specific and show you filter noise. "I follow a few high quality sources instead of everything: release notes from the main model providers, a couple of newsletters, and papers when something important comes up. But mostly I learn by building: when something new appears, like a new tool calling feature, I try it on a small project within a week and write a short note about what worked. I also review what changed in our own evals when we upgrade models, which teaches me more than reading headlines."

Question 36

How do you use AI coding assistants in your work, and how do you make sure the code is right?

Accepted Answer

Be honest and responsible. "I use AI assistants daily for boilerplate, tests, unfamiliar libraries and explaining code. But I treat the output like code from a new colleague: I read every line before accepting it, I make sure there are tests, I check security sensitive parts like authentication and SQL myself, and I never paste secrets or confidential data into tools that aren't approved. It makes me faster, but I'm still responsible for what I ship."

Question 37

Do you have any questions for us?

Accepted Answer

Always say yes and ask two or three real questions. Good ones: "What would success look like for this role in the first 6 months?", "What's the biggest challenge the team is facing with AI right now?", "How do you evaluate model or feature quality before shipping?", "How is the team split between building new features and maintaining existing ones?", and "What do people who do well here have in common?". Avoid asking about leave or salary in a technical round, and don't ask things that are on their website.

Question 38

What are your salary expectations?

Accepted Answer

Research first, using sites like Glassdoor, AmbitionBox, levels.fyi and people in similar roles, then give a range based on the role, not on your current pay. "Based on my research for this role and level in Bangalore, and my experience, I'm looking at a range of ₹22 to ₹28 lakh fixed. I'm flexible depending on the overall package, and I'm most interested in the role itself." If they insist on your current salary, you can share it, but steer back to the market range for the new role. Never accept or reject on the spot; ask for the offer in writing.

Question 39

What does len({'a': 1, 'b': 2, 'a': 3}) return?

Accepted Answer

2. Dictionary keys are unique. The second 'a' overwrites the first, leaving 2 keys.

Question 40

You need to check whether 1 million IDs are in a collection of 1 million known IDs. What's the efficient approach?

Accepted Answer

Put the known IDs in a set and use `in`. Membership checks in a set are O(1) on average, but O(n) in a list. With a list this becomes about a trillion comparisons.

Question 41

What's wrong with `def add(item, basket=[]): basket.append(item); return basket`?

Accepted Answer

The default list is created once and shared between calls, so items leak across calls. Default values are evaluated once, when the function is defined. Use `basket=None` and create a new list inside the function.

Question 42

What does df.groupby('city')['amount'].mean() return?

Accepted Answer

One average amount per city. groupby splits the rows by city, then mean() averages 'amount' within each group, giving a Series indexed by city.

Question 43

Salaries in a team are mostly around ₹8 LPA, with one executive at ₹2 crore. Which best describes a typical salary?

Accepted Answer

The median. One extreme value pulls the mean up a lot. The median, the middle value, isn't affected by outliers.

Question 44

Users who turned on a new 'focus mode' spend 30% more time in the app. Can you say focus mode caused it?

Accepted Answer

No. People who opt in may already be more engaged, so run a randomised A/B test. This is self-selection: the opt-in group differs from everyone else. Only random assignment isolates the feature's effect.

Question 45

You want only the cities with more than 100 orders. Which clause filters those groups?

Accepted Answer

HAVING COUNT(*) > 100. WHERE filters rows before grouping. HAVING filters groups after aggregation.

Question 46

How do you find customers who have never placed an order?

Accepted Answer

LEFT JOIN orders and keep rows where the order id IS NULL. A LEFT JOIN keeps every customer. Those with no match have NULL order columns.

Question 47

Training accuracy is 98% and test accuracy is 65%. What's happening, and what's a reasonable fix?

Accepted Answer

Overfitting, so simplify the model, regularise or get more data. A large train/test gap means the model memorised the training data. Reduce its complexity, regularise, or add data.

Question 48

A fraud model is 99% accurate, and 1% of transactions are fraud. What's your first reaction?

Accepted Answer

Compare against the baseline and check precision and recall, because predicting 'not fraud' for everything also scores 99%. With rare classes, accuracy is misleading. Precision and recall show whether fraud is actually being caught.

Question 49

A fraud model uses 'refund_issued' as a feature and scores 0.99 AUC in testing. What's the problem?

Accepted Answer

Data leakage: refunds happen after fraud is confirmed, so this information isn't available at prediction time. Features must only use information available at the moment of prediction. Otherwise test scores are inflated.

Question 50

For a churn model on a 30-column customer table, what's a strong first choice after a baseline?

Accepted Answer

Gradient-boosted trees such as XGBoost or LightGBM. Boosted trees are usually the most accurate, fastest and most explainable option for tabular data.

Question 51

For a cancer-screening model, which metric should you usually prioritise?

Accepted Answer

Recall. Missing a real case is far more costly than a false alarm, which only leads to another test.

Question 52

You need consistent, repeatable answers for extracting invoice fields. How should you set temperature?

Accepted Answer

Low (around 0). Low temperature makes outputs focused and repeatable. Higher temperature adds variety, which suits brainstorming.

Question 53

Why do LLMs sometimes state wrong facts confidently?

Accepted Answer

They generate plausible text and don't verify facts unless given sources or tools. Grounding answers in sources (RAG), using tools, and showing citations reduces hallucination.

Question 54

Tell me about yourself and why you're moving into this AI role.

Accepted Answer

I'm a backend engineer with 8 years in fintech, building payment APIs that handle about 2 million requests a day. Over the last six months I've been moving into LLM engineering: I built a support-triage agent with tool calling and an eval suite, which raised correct routing from 71% to 89% on 200 real tickets. This role fits because it combines what I'm already good at, reliable APIs and production debugging, with the agent work I enjoy most. In the first months I'd bring production discipline: tracing, evals and cost controls for your agents.

Question 55

Walk me through an AI or ML project you built end to end.

Accepted Answer

I built a policy assistant for branch staff who spent about 25 minutes a day searching PDFs. I indexed 140 policy documents with chunking and metadata, used hybrid search, and required citations with a 'not found' rule. I chose RAG over fine-tuning because the policies change monthly. On a 60-question eval set built with staff, correct-and-cited answers went from 68% to 90% after I added re-ranking. It's deployed as an internal web app. The main failure was tables in PDFs; next I'd add table-aware parsing.

Question 56

Tell me about a time something you built failed. What did you do?

Accepted Answer

Our nightly data pipeline silently loaded duplicate orders for three days, inflating revenue on a dashboard by 12%. I owned that pipeline. I traced it to a retry that re-appended a partial load, so the job wasn't idempotent. I fixed the data, switched the load to overwrite-by-partition, and added a uniqueness check that fails the run. Since then we've had no duplicate incidents, and I now design every pipeline to be safely re-runnable.

Question 57

Design a fraud-detection system for UPI payments.

Accepted Answer

I'd start from requirements: decisions in under 100 ms at peak volume, and an explicit cost for missed fraud vs blocking a genuine payment. Features combine real-time signals, such as amount versus the user's usual, transactions in the last 10 minutes and a new device or payee, with historical aggregates from a feature store, all computed only from data available at payment time. A gradient-boosted model fits tabular signals well. I'd set the threshold to minimise expected cost and send a middle band to manual review. It would be served behind a low-latency API with streaming feature updates. I'd monitor input drift and precision and recall as chargeback labels arrive weeks later, and retrain on a schedule.

Question 58

Explain overfitting to a non-technical person, and how you'd detect and fix it.

Accepted Answer

Overfitting is like a student who memorises last year's paper word for word and then struggles with new questions. To detect it, I keep a test set the model never sees: if training accuracy is 98% but test accuracy is 70%, it has memorised rather than learned. Cross-validation gives a more reliable estimate. To fix it, I'd use a simpler model or regularisation, get more or cleaner data, or stop training earlier.

Question 59

In simple words, what is an LLM, and what is one important limitation?

Accepted Answer

An LLM is a model trained on a huge amount of text to predict the next word, or token. Doing that at scale lets it write, summarise and answer questions. It works within a context window, a limit on how much text it can consider at once. A key limitation is that it produces plausible text rather than checked facts, so it can confidently state something wrong. You reduce this by giving it trusted sources at question time (RAG), asking for citations, and using tools for calculations or live data.

AI Foundations Path interview questions and answers

How an AI Foundations Path interview usually goes

👋 Opening: about you

🧱 Fundamentals

🛠️ Role skills

🏗️ Design and case questions

🤝 Projects and behaviour

❓ Closing: your questions and salary

🧪 Quick quiz: test yourself

Python & coding

Statistics & data

SQL & data modelling

ML fundamentals

LLMs & prompting

Communication

ML system design

More questions to prepare

Practise with a score and gap analysis

Also for AI Foundations Path

Sources