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Built a few ML models, tuned some hyperparameters,yet still feel unprepared for interviews?
You're not wrong. The bar for AI/ML roles today is higher than ever.
Top companies aren’t hiring people who know algorithms.
They’re hiring people who can solve business problems with algorithms.
This guide doesn’t waste time on textbook trivia.
We’ve curated 10 real-world interview questions that hiring managers at companies like Google, TCS, and Razorpay are actually asking.
Each question comes with a model answer, plus exactly what they’re testing you for, and how to avoid common traps.
Because in AI interviews, it’s not about what you’ve learned. It’s about what you can deploy.
Also Read: 10 Real-World AI Projects You Can Build in 2025
Artificial Intelligence, or AI, refers to the ability of machines to perform tasks that typically require human intelligence. These tasks include reasoning, learning, decision making, language understanding, and perception.
But AI is not a single technology, it’s an umbrella term that includes everything from simple rule-based systems to advanced models like ChatGPT.
Also Read: Top 10 Prompt Engineering Courses in 2025
Let’s proceed with the questions.
This is a warm-up question, but it quickly reveals whether a candidate truly grasps the foundational hierarchy or is just repeating buzzwords. Recruiters want clarity, not jargon.
Artificial Intelligence (AI) is the broad goal: making machines think and act like humans.
Machine Learning (ML) is a subset of AI focused on systems that learn from data.
Deep Learning (DL) is a subset of ML that uses neural networks to learn complex patterns, especially from unstructured data like images or text.
Think of it like this:
Example:
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This question tests whether you understand model behavior beyond surface-level performance. Interviewers want to know: Can you debug poor results? Can you tune models smartly?
Bias is error from overly simplistic assumptions, a high-bias model underfits the data.
Variance is error from sensitivity to small data changes, a high-variance model overfits.
The bias-variance trade-off is about finding a sweet spot:
Imagine fitting a line to a curve (high bias) vs. a wiggly line that memorizes every point (high variance).
Neither performs well on test data. The goal is balance.
Draw a U-shaped curve with model error on the y-axis, model complexity on the x-axis.
Know More: Top 10 AI Skills in Demand in May 2025
This checks whether you can match the right learning type to the right problem, and explain it clearly. It’s a basic, but critical, decision-making skill in real-world ML roles.
Supervised learning learns from labeled data.
You feed the model input-output pairs, and it learns to map input to output.
Example: Predicting house prices using past sale data.
Unsupervised learning finds patterns in unlabeled data.
It groups or compresses data without predefined outcomes.
Example: Customer segmentation using purchase behavior.
Reinforcement learning is based on feedback through rewards.
An agent learns by interacting with an environment and optimizing for long-term rewards.
Example: A robot learning to walk, or an AI playing chess.
If you want hands-on exposure to all three learning paradigms, along with real-world deployment pipelines and industry use cases, check out the IIT Jodhpur PGD and M.Tech in Artificial Intelligence program powered by Futurense. It's designed to bridge theoretical depth with enterprise application.
This isn’t about naming algorithms. It’s about showing that you can evaluate real-world constraints: data type, interpretability, performance trade-offs and make informed choices.
There’s no “best” algorithm. The right choice depends on:
Example:
If you’re predicting loan defaults with thousands of rows and need explainable results, start with logistic regression or decision trees. If accuracy is more important and you have compute, try XGBoost.
Most real-world datasets aren’t cleanly balanced: fraud detection, medical diagnosis, churn prediction are all skewed. Interviewers want to know if you can deliver reliable results even when one class dominates.
Imbalanced datasets can cause models to overpredict the majority class. To handle this, I typically use a combination of:
class_weight=‘balanced’.
Example:
In a credit card fraud detection task with only 0.5% fraud cases, I used SMOTE to generate synthetic fraud samples and tuned for F1-score instead of accuracy.
Even with fancy algorithms, poor features = poor results. This question helps interviewers assess whether you can shape the raw data into predictive gold, not just run models.
Feature engineering is the process of creating input variables that make models smarter. It’s the bridge between raw data and model performance.
Key aspects include:
Example:
In a telecom churn prediction project, simply creating a “tenure × plan type” interaction improved AUC by 5%.
Anyone can train a model. The real test is whether you can measure success the right way and adapt based on the business context. This question reveals how well you align ML with outcomes.
Model evaluation depends on the problem type (classification vs. regression) and the real-world cost of errors.
For classification:
For regression:
Example:
For a churn model at a telecom company, I optimized for recall, missing a churner costs more than mistakenly flagging a loyal user.
This question tests whether you understand how to control overfitting, a critical skill when working with high-dimensional data or complex models.
Regularization is the technique of adding a penalty to a model’s loss function to discourage overfitting by shrinking the model's coefficients.
Example:
In a credit scoring model with hundreds of features, I used L1 regularization to drop irrelevant variables and improve interpretability. For a linear regression on smaller, clean data, L2 helped reduce variance and improve generalization.
A model that works in a notebook isn’t enough. Interviewers want to see if you can take it all the way, from prototype to production, with monitoring and scaling in mind.
Deploying a model involves several critical steps:
Example:
For a real-time lead scoring model, I used FastAPI + Docker, deployed it on AWS ECS, and monitored model drift using EvidentlyAI.
This question isn’t about failure, it’s about ownership, debugging skills, and whether you learn from the messy side of machine learning. It separates mature candidates from superficial ones.
In a sales forecasting project, I trained a regression model on historical transaction data.
It showed strong performance in cross-validation but failed in production.
What went wrong:
What I learned:
What I did next:
1. Think in trade-offs, not in terms of “best”
There’s no one-size-fits-all model or metric. Always ask: What matters more, speed, accuracy, or explainability?
2. Communicate like a consultant, not a coder
Hiring managers want engineers who can explain results to product teams, not just tune hyperparameters.
3. Showcase your projects with context
Don’t just say “I built a model with 92% accuracy.” Explain the problem, data constraints, business impact, and what went wrong before it worked.
4. Practice debugging aloud
Mock interviews should include diagnosing overfitting, feature leakage, and data drift, these are common evaluation scenarios.
5. Stay current, but be grounded
Yes, LLMs are exciting. But if you're interviewing for a churn prediction role, don’t talk about GPT-4 unless you can link it to value.
6. Learn the tooling stack
Beyond models, interviewers now expect familiarity with:
Want structured guidance on mastering these tools in a job-aligned curriculum? The IIT Jodhpur PGD and M.Tech in AI by Futurense integrates them into a full-stack AI education, tailored for high-performance AI careers.
7. Own the why behind every choice
Why this algorithm? Why this metric? Why this deployment flow? Good answers win interview
Python is a must. SQL for data handling, and some exposure to R or C++ can help depending on the domain (e.g., C++ for embedded ML, R for statistical modeling).
Expect a mix. Entry-level roles lean toward practical applications and model use. Research or senior roles often go deeper into linear algebra, probability, and optimization.
At a minimum:
Focus on building data pipelines, model retraining logic, versioning, CI/CD, and monitoring. Practice explaining how you'd deploy a model end-to-end.
For classification: Precision, Recall, F1-score, ROC-AUC, PR-AUC.
For regression: MAE, RMSE, R².
Choose based on business impact and data imbalance.
Use AutoML for rapid prototyping, but show you understand what’s happening under the hood. You should be able to explain feature selection, model choice, and evaluation logic.
Use real-world datasets (e.g., Kaggle, UCI), deploy models (via Streamlit or Flask), and document everything on GitHub or a portfolio site with clear problem framing.
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“Honestly, I was sceptical at first. But the Tech Talk series changed my mind.. And I couldn’t have done any better than with Futurense!”
“Worked at a very low-paying job but couldn’t complain as my skills were not up to the mark. Futurense saved me with their “get paid to learn” model.”
“Talking about building teams, I feel Futurense is playing a crucial role in it by providing tech talent that fits like the perfect piece in the jigsaw puzzle.”
Employees
Delhi | Bengaluru
Employees
Delhi | Bengaluru
“Talking about building teams, I feel Futurense is playing a crucial role in it by providing tech talent that fits like the perfect piece in the jigsaw puzzle.”
“Stepping beyond the traditional way of hiring resources, training them, and then deploying them to me is one of the best processes that Futurense has.”
“Worked at a very low-paying job but couldn’t complain as my skills were not up to the mark. Futurense saved me with their “get paid to learn” model.”
“Honestly, I was sceptical at first. But the Tech Talk series changed my mind.. And I couldn’t have done any better than with Futurense!”
