Beginner AI Interview Questions

Artificial Intelligence (AI) is the simulation of human intelligence in machines programmed to think, learn, and solve problems.

Generative AI refers to AI systems that can create new content such as text, images, audio, and video based on patterns learned from training data.

Machine Learning is a subset of AI where algorithms learn from data to make predictions or decisions without being explicitly programmed.

Deep Learning is a subset of machine learning that uses neural networks with many layers to model complex patterns in data.

A neural network is a computational model inspired by the human brain, consisting of interconnected nodes (neurons) that process and transmit information.

NLP is a branch of AI that enables machines to understand, interpret, and generate human language.

An LLM is a deep learning model trained on massive amounts of text data to understand and generate human-like language.

A prompt is an input or instruction given to an AI model to guide it toward generating a desired output.

Training data is the dataset used to teach a machine learning model to recognize patterns and make predictions.

A model is a mathematical representation trained on data to perform tasks such as classification, prediction, or content generation.

Supervised learning is a type of machine learning where the model is trained on labeled data with known input-output pairs.

Unsupervised learning is a type of machine learning where the model finds patterns in unlabeled data without predefined outputs.

Reinforcement learning is a type of machine learning where an agent learns by interacting with an environment and receiving rewards or penalties.

A chatbot is an AI-powered application that simulates human conversation through text or voice interactions.

Computer Vision is a field of AI that enables machines to interpret and understand visual information from images and videos.

A dataset is a structured collection of data used to train, validate, or test machine learning models.

Overfitting occurs when a model learns training data too well, including noise, and performs poorly on new unseen data.

Underfitting occurs when a model is too simple to capture the underlying patterns in the data, resulting in poor performance.

A token is a unit of text (such as a word or character) that an AI model processes as part of its input or output.

AI is the broadest concept of intelligent machines; ML is a subset using data-driven learning; Deep Learning is a subset of ML using multi-layered neural networks.

A Transformer is a neural network architecture that uses self-attention mechanisms to process sequential data, forming the backbone of most modern LLMs.

Prompt engineering is the practice of crafting and optimizing prompts to guide AI models toward accurate and relevant outputs.

Fine-tuning is the process of further training a pre-trained model on a specific dataset to adapt it for a particular task or domain.

Transfer learning reuses a model trained on one task as a starting point for a different but related task, saving time and resources.

RAG is a technique that combines a retrieval system with a generative model to produce more accurate and contextually grounded responses.

Embeddings are numerical vector representations of data (such as words or images) that capture semantic meaning for use in AI models.

A vector database stores and retrieves data as high-dimensional vectors, enabling fast similarity search used in AI applications.

The attention mechanism allows a model to focus on the most relevant parts of the input when generating each part of the output.

Zero-shot learning is the ability of a model to perform tasks it was never explicitly trained on, using only a description or context.

Few-shot learning is when a model learns to perform a task from only a small number of examples provided in the prompt or training.

Hallucination is when an AI model generates information that sounds plausible but is factually incorrect or fabricated.

AI bias occurs when a model produces systematically skewed results due to biased training data or flawed model design.

A diffusion model is a generative AI model that learns to create data (such as images) by reversing a process of gradually adding noise.

A GAN consists of two neural networks — a generator and a discriminator — that compete to produce increasingly realistic synthetic data.

The context window is the maximum amount of text (measured in tokens) that an AI model can process in a single input-output interaction.

Temperature is a parameter that controls the randomness of an AI model's output — higher values produce more creative responses while lower values produce more deterministic ones.

RLHF is a training technique where human evaluators rank model outputs, and those rankings are used to fine-tune the model to better align with human preferences.

An AI agent is an autonomous system that perceives its environment, makes decisions, and takes actions to achieve specific goals.

Multimodal AI can process and generate multiple types of data such as text, images, audio, and video within a single model.

Model evaluation is the process of measuring a model's performance using metrics such as accuracy, precision, recall, and F1 score.

AI alignment is the challenge of ensuring AI systems behave in ways that are consistent with human values, intentions, and goals.

Explainable AI refers to methods and techniques that make AI model decisions interpretable and understandable to humans.

Constitutional AI is a training approach where the model is guided by a set of principles to produce helpful, harmless, and honest outputs.

Model distillation is a compression technique where a smaller student model is trained to replicate the behavior of a larger teacher model.

Quantization reduces the precision of model weights (e.g., from 32-bit to 8-bit) to decrease model size and increase inference speed with minimal accuracy loss.

Federated learning trains a model across multiple decentralized devices or servers without sharing raw data, preserving privacy.

Narrow AI (ANI) is designed for specific tasks, while General AI (AGI) would possess human-level intelligence applicable across any domain.

AI ethics encompasses principles and guidelines to ensure AI is developed and used fairly, transparently, safely, and without harm.

Agentic AI refers to systems capable of autonomous multi-step reasoning, planning, and executing complex tasks with minimal human intervention.

Chain-of-thought prompting encourages the model to reason step by step before arriving at a final answer, improving accuracy on complex tasks.

Model drift occurs when a deployed model's performance degrades over time due to changes in real-world data distributions.

Responsible AI is a framework for designing, developing, and deploying AI systems that are safe, fair, transparent, and accountable.

NAS automates the design of optimal neural network architectures by searching over a space of possible configurations.

MLOps (Machine Learning Operations) is a set of practices that streamlines the deployment, monitoring, and maintenance of ML models in production.

Synthetic data is artificially generated data that mimics real-world data, used to train models when real data is scarce or sensitive.

Mitigate bias through diverse training data, fairness audits, bias detection tools, and continuous monitoring of model outputs.

Scaling laws describe how model performance improves predictably as model size, training data, and compute are increased.

MoE is a model architecture that activates only a subset of specialized sub-networks (experts) for each input, improving efficiency at scale.

Grounding connects AI model outputs to real-world knowledge or data sources to reduce hallucination and improve factual accuracy.

Evaluate using automated metrics (BLEU, ROUGE), human evaluation, factual accuracy checks, and task-specific benchmarks.