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Azure Databricks has become a core platform for building modern Lakehouse architectures on Azure. Interviewers today focus less on definitions and more on real-world implementation, performance, governance, and cost optimization.
This blog presents a curated and logically sequenced list of Azure Databricks interview questions, covering ingestion, transformation, governance, CI/CD, streaming, security, and architecture — exactly how interviews flow in real scenarios.
This blog is based on actual questions asked in a TCS Data Engineer Interview (Round 1) and, more importantly, explains what interviewers really expect behind each question. If you are preparing for TCS or similar data engineering roles, this guide will help you align your answers with practical thinking rather than theoretical explanations.
When people talk about AI Agents or Agentic AI, one question often comes up:
“What actually makes an AI agent intelligent?”
The short answer is: Large Language Models (LLMs).
LLMs are often called the “brain” of modern AI agents — and for good reason. Let’s understand why in simple terms:
If you’ve ever wondered how ChatGPT, Gemini, or Claude can understand long sentences, follow context, and give human-like replies — the secret lies in a powerful deep learning architecture called the Transformer.
Let’s break it down step-by-step:
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.
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