IInterview preparation for AI roles in 2026 should focus on core technical knowledge, an understanding of current generative AI trends, practical MLOps skills, and critical ethical considerations.
Fundational AI Concepts
These questions assess your basic understanding of the AI landscape.
Q: What is the difference between AI, Machine Learning (ML), and Deep Learning (DL)?
A: AI is the broad field of creating machines that mimic human intelligence. ML is a subset of AI where systems learn from data to identify patterns and make decisions without explicit programming. DL is a subset of ML that uses deep neural networks (multiple layers) to solve complex problems like image and speech recognition.
Q: Explain supervised, unsupervised, and reinforcement learning.
A: Supervised learning uses labeled data to train models to make predictions or classifications (e.g., spam filtering). Unsupervised learning uses unlabeled data to find hidden patterns or structures (e.g., clustering customer segments). Reinforcement learning involves an agent learning through interaction with an environment to maximize a cumulative reward (e.g., training a self-driving car).
Q: Explain the bias-variance tradeoff.
A: This fundamental concept involves balancing model complexity. Bias refers to the error from overly simplistic assumptions (underfitting), while variance refers to error from a model being too sensitive to minor fluctuations in training data (overfitting). The goal is to find the optimal balance for good performance on unseen data.
Q: What is a neural network, and what is the role of an activation function?
A: A neural network is a series of interconnected layers inspired by the human brain that processes data. An activation function introduces non-linearity into the network, allowing it to learn complex patterns and map arbitrary functions, which a simple linear model cannot do.
Generative AI and LLMs (Crucial for 2026)
The current job market emphasizes generative AI knowledge.
Q: What is Retrieval-Augmented Generation (RAG), and why is it important?
A: RAG is an AI framework that improves the output quality of a large language model (LLM) by retrieving facts from an external knowledge base to ground its responses in accurate, current information. This helps mitigate hallucinations (AI generating convincing but false information) and ensures the model uses relevant data beyond its initial training cutoff.
Q: Define “hallucination” in generative AI and how you might mitigate it.
A: Hallucination is when an AI model generates content that is factually incorrect or nonsensical but presented confidently. Mitigation techniques include using RAG systems, prompt engineering to provide more context, fine-tuning models on specific, high-quality data, and implementing safety layers or guardrails in the application interface.
Q: Explain the concept of “prompt engineering.”
A: Prompt engineering is the practice of carefully designing inputs (prompts) for a generative AI model to guide its output to be more accurate, relevant, or creative. It involves crafting clear, specific instructions and providing context or examples (few-shot prompting) to achieve desired results.
Scenario and Role-Specific Questions
These questions test your practical application of AI knowledge.
Q: How do you handle an imbalanced dataset in a machine learning project?
A: I would first analyze the severity of the imbalance and decide on appropriate metrics like precision, recall, or F1-score rather than simple accuracy. Techniques I might use include oversampling the minority class (e.g., using SMOTE), undersampling the majority class, or adjusting class weights during model training.
Q: How do you ensure ethical and responsible AI use at scale?
A: This involves a multi-faceted approach including establishing clear AI governance frameworks, performing rigorous bias detection and mitigation during development and deployment, ensuring model transparency and explainability (Explainable AI – XAI), and incorporating human-in-the-loop systems for critical decisions.
Q: How would you approach designing an AI system to detect fake news?
A: I would likely use Natural Language Processing (NLP) techniques, potentially involving transformer models to analyze text semantics, source credibility, and propagation patterns. Key challenges include acquiring a balanced, diverse dataset for training, handling evolving misinformation tactics, and addressing the ethical risk of censorship or bias in the detection system itself.
Q: How do you stay updated with the fast-evolving AI landscape?
A: I actively follow industry publications, attend relevant conferences (virtually or in-person), participate in online courses, and experiment with new open-source models and frameworks like PyTorch or TensorFlow.
