AI and ML Interview Questions: What Freshers Need to Know

AI and ML interview questions appear in TCS Digital, Infosys DSE, and most product-company technical screens in 2026, but the fresher bar is conceptual, not a graduate seminar in statistics.

This article maps where AI/ML shows up in your placement pipeline, what questions you will actually get asked, and how to prepare without disrupting your aptitude and coding work. Per TCS CHRO Sudeep Kunnumal at the AI Impact Summit in March 2026, 60% of TCS’s FY26 fresher hires were AI-skilled. That shift is showing up in interview formats: digital-track technical screens now include AI/ML concept questions that were not standard three years ago.

Which company tracks test AI and ML

Not every role requires AI/ML knowledge. The split is between service-tier tracks, which test aptitude and coding, and digital-tier tracks, which add AI/ML concepts to the technical screen. The IT sector is on track to add more than 150,000 fresher roles in FY26, with the fastest growth in digital and AI-adjacent roles.

Recruiter	Track	AI/ML in technical screen?	Approximate CTC
TCS	Ninja	No	~3.36 LPA
TCS	Digital	Yes (conceptual)	~7 LPA
TCS	Prime	Yes (applied, includes coding)	~11 LPA
Infosys	SE (standard)	No	~3.6 LPA
Infosys	DSE / Power Programmer	Yes (conceptual)	~9.5 LPA
Cognizant	GenC	No	~4 LPA
Cognizant	GenC Pro	Yes (conceptual)	~6 LPA

If your placement target is a service-tier role, AI/ML prep is optional. Once you are targeting the 6-plus LPA digital tracks, the conceptual bar becomes a real screen criterion.

What companies actually ask freshers on AI and ML

The pattern across IT services digital tracks is consistent: interviewers ask definitional questions and test whether you can explain a concept clearly, not whether you can derive it mathematically. The table below maps the ten questions that appear most frequently in digital-track technical screens.

Question	One-line answer to aim for
What is the difference between AI and ML?	AI is the broader goal of machine intelligence; ML is one method that achieves it by learning from data.
What is supervised learning?	Training a model on labeled input-output pairs so it learns to predict outputs for new inputs.
What is unsupervised learning?	Finding patterns in data without predefined labels (clustering, anomaly detection).
What is overfitting?	When a model memorizes training data too closely and performs poorly on new examples.
How do you reduce overfitting?	Use more data, apply regularization, or use cross-validation to evaluate generalization.
What is a decision tree?	A model that splits data on feature conditions, following branches to reach a prediction.
What is gradient descent?	An optimization method that adjusts model parameters step-by-step to minimize prediction error.
What is a neural network?	Layers of interconnected nodes that learn weighted patterns from data through repeated training.
What is the bias-variance tradeoff?	High bias means underfitting; high variance means overfitting. Good models balance both.
What is cross-validation?	Splitting data into multiple train-test folds to test model performance more reliably.

For most Tier-2 and Tier-3 college students, preparing solid answers to these ten questions covers the conceptual floor for TCS Digital and Infosys DSE. Product company interviews for AI-specific roles go further, but that is a separate preparation track.

What the higher-tier roles look for beyond concepts

Conceptual answers clear the digital-track bar. Product companies and AI-specific roles at Global Capability Centres raise the bar further. The distinguishing signal there is a working project.

A deployed ML model on a public GitHub repository tells an interviewer more than three MOOC certificates, because it shows the candidate has moved from watching a course to producing a result. This does not mean building something research-level. A text classification model, a recommendation system for a small dataset, or a data pipeline that processes and visualises a publicly available dataset all demonstrate the core workflow: loading data, cleaning it, fitting a model, and evaluating whether it generalised.

For engineering students at Tier-2 and Tier-3 colleges across India, this signal matters most when applying for fresher IT roles in Bangalore and the other major tech hiring cities, where GCC and funded startup openings increasingly treat the GitHub project as a first filter before the technical interview.

How to prep for AI and ML within a placement timeline

A 4 to 6 week focused block, running alongside aptitude and coding preparation, covers the conceptual bar for most digital-track interviews. The sequence that works:

• Weeks 1 and 2: Cover the ML fundamentals. Study supervised learning, unsupervised learning, and the key algorithms (decision trees, random forests, linear and logistic regression). Work through the ten questions in the table above until you can answer each clearly in under a minute.
• Week 3: Pick up Python if you have not already, and run one end-to-end ML workflow using scikit-learn on a public dataset. The Iris or Titanic datasets from Kaggle are standard starting points.
• Weeks 4 to 6: Build one small project. Classify text, cluster customer data, or predict a numerical outcome. Push it to GitHub with a readable README explaining what the model does and how you evaluated it.

The coding foundation runs alongside AI/ML, not separately. Most digital-track interviews still include a coding round, and Java is the most common language choice for IT services technical rounds. For that layer, the most frequently asked Java interview questions are a focused prep target that you can work through in parallel.

The 4 to 6 week window gets you to the conceptual placement bar. Going deeper, to the point where you can build and deploy AI applications beyond coursework projects, is a different commitment. TinkerLLM at ₹299 is a practical starting point for students who want to test hands-on project work at low cost before committing to a longer path.