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Amazon Redshift is one of the most popular cloud data warehouses used in modern big-data architectures. Whether you are building ELT pipelines, performing analytics at scale, or optimizing workloads, Redshift is a crucial skill for Data Engineers.
In this article, I’ve compiled the 25 most frequently asked AWS Redshift interview questions, along with answers.
Back in my college days, most students were busy mastering C++ or Java, because every company visiting the campus preferred those languages for coding rounds.
Even my faculty advised me to pick Java and “at least become an expert in it.”
But I had already made my decision.
I chose Python — and decided to stick with it.
When we talk about AI tools like ChatGPT, Gemini, or Copilot, we often hear two terms used almost interchangeably —
Generative AI and Large Language Models (LLMs).
But are they the same thing?
Not exactly.
Let’s understand the difference — in simple language.
If you’ve ever used ChatGPT, Gemini, or Claude, you’ve already interacted with what’s called a Large Language Model (LLM).
But have you ever wondered — how do these models actually work? How can they write essays, answer questions, or even generate code like humans?
Let’s break it down step by step — in simple language.
If you’ve used ChatGPT, Gemini, or Claude, you’ve already interacted with what’s called a Large Language Model, or LLM.
But what exactly is an LLM?
And how is it different from regular AI models?
Let’s understand this concept.
If you’ve been following the rise of Artificial Intelligence, you might have heard new buzzwords like AI Agents and Agentic AI
These terms sound fancy — but what do they actually mean? Let’s break them down in simple words.
If you’ve been following the world of Artificial Intelligence lately, you’ve probably heard the term “Generative AI” everywhere — from ChatGPT to AI art to music generation.
But what exactly is Generative AI?
How is it different from traditional AI or Machine Learning?
Let’s break it down step by step in simple language.
When learning Machine Learning, two of the first terms you’ll encounter are Supervised Learning and Unsupervised Learning.
These are the two main ways machines learn from data — and understanding their difference is key to mastering AI.
If you’ve just started exploring AI and ML, you’ve probably come across terms like Statistical Learning and Deep Learning. They sound similar, but they actually represent two very different approaches to solving problems with data.
When people hear Artificial Intelligence (AI), they often think of Machine Learning (ML) — and sometimes use the two terms interchangeably. But while they’re closely related, AI and ML are not the same.
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