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  This tests: System design depth Understanding of distributed systems Trade-off navigation (CAP, consistency, latency) Real-world edge case handling Let’s go step by step and design Redis-like cache from first principles , not using cloud-managed services. 🚀 Goal: Build a Redis-like Distributed In-Memory Cache 🧾 1. Requirements Gathering (Clarify with interviewer) 🔹 Functional Support GET , SET , DEL , TTL Handle concurrent reads/writes Cache keys across multiple nodes Optional: Support pub/sub, data structures (hash, list) 🔹 Non-Functional Low latency (<1ms typical) High availability & fault tolerance Scalable horizontally Eventual or strong consistency Memory-optimized with TTL eviction Absolutely! Back-of-the-envelope estimations are crucial in system design interviews — they demonstrate your pragmatism , ability to roughly size a system, and to make sound trade-offs . Let’s break it down for your Redis-like...

  Design a Global Video Streaming Service (e.g., YouTube, Netflix) Question: Design a scalable and fault-tolerant video streaming platform that can: Stream videos globally with low latency. Allow users to upload videos. Handle millions of users simultaneously. Requirements (functional and non-functional) functional requirement: video upload, video streaming, handle the network bandwidth, video for the different devices like mobile, smart tv, computer non-function : high availability, fault tolerance 1. High-Level Requirements Functional Requirements: Video Upload : Users can upload videos in various formats. Video Streaming : Provide smooth playback with adaptive streaming for different network conditions. Network Bandwidth Handling : Adjust video quality dynamically based on bandwidth. Device Compatibility : Support multiple devices (e.g., mobile, smart TV, computer). Non-Functional Requirements: High Availability : The service should handle millions of concurrent viewers with m...

Question:  Imagine you are tasked with designing a system similar to Bitly that converts long URLs into short ones. The system should handle billions of URLs and millions of requests per second. Please explain how you would design this system. Requirements: function requirements : shortener, redirection, expiry of url  non-functional : high availability, fault tolerant (like AP from CAP) Step 1: High-Level Design At a high level, the system will have the following components: Frontend Service : Handles user requests for shortening, redirection, and URL expiry. Backend Service : Processes requests, generates short URLs, manages expiration policies, and stores mappings. Database : Stores the short-to-long URL mappings. Cache : Speeds up redirection for frequently accessed URLs. Load Balancer : Distributes incoming traffic evenly across backend servers to handle high availability and fault tolerance. Step 2: Capacity Planning Now, let's expand on capacity planning for this system...

To excel in a system design interview at Google India , you’ll need a structured, methodical approach while demonstrating clarity and confidence. Here’s how you can handle system design questions effectively: 1. Understand the Problem Statement Before diving in, clarify the requirements: Ask questions to understand functional requirements (e.g., "What features does the system need?"). Explore non-functional requirements like scalability, performance, reliability, and security. Example: If asked to design a URL shortener, clarify if analytics tracking or expiration for URLs is required. 2. Start with a High-Level Approach Begin by breaking the problem into logical components . Use simple terms initially: For example: "For a URL shortener, we need to generate short URLs, store mappings, and support quick redirections." Draw a rough block diagram : Show user interaction, application servers, caching layers, databases, etc. Use terms like "user sends request,...

  🎯 Design a Scalable System to Monitor AI/ML Training Workloads 🚀 Prompt: Design a system that monitors distributed AI/ML training jobs across thousands of compute nodes (e.g., GPUs/TPUs) running in Google Cloud. The system should collect, process, and surface metrics like: GPU utilization Memory consumption Training throughput Model accuracy over time It should support real-time dashboards and alerts when anomalies or performance degradation are detected. 🔍 1. Clarifying Questions Ask these before diving into design: How frequently should metrics be collected? (e.g., every second, every minute?) Are we targeting batch training jobs, online inference, or both? Do we need historical analysis (long-term storage), or just real-time? Should users be able to define custom metrics or thresholds? 🧱 2. High-Level Architecture [ML Training Nodes] | | (Metrics via agents or exporters) v [Metrics Collector Service] | | (K...

 Preparing for a senior engineering leadership interview at Google India is an exciting challenge! Based on the references you provided and additional insights, here are some tailored questions and answers to help you prepare: Leadership & Behavioral Tell me about a time you had a difficult team member. How did you handle the situation? What were the outcomes? Answer : Use the STAR method. For example, describe a situation where a team member was resistant to feedback. Explain how you approached them with empathy, provided constructive feedback, and set clear expectations. Highlight the positive outcomes, such as improved collaboration and project success. Describe your leadership style. How do you motivate and guide your team? Answer : Share your leadership philosophy, emphasizing adaptability, empowerment, and clear communication. Provide examples of how you’ve motivated your team, such as recognizing achievements or fostering a culture of innovation. Project Management How d...

 Fine-tuning a pre-trained LLM like GPT is an exciting step, as it allows you to adapt an existing model to specific tasks. Let’s get started! What is Fine-Tuning? Fine-tuning adjusts the weights of a pre-trained model to specialize it for a particular task. For example: A customer service chatbot A legal document summarizer A creative writing assistant What Tools and Libraries Do You Need? Python : Our programming language. Hugging Face's Transformers Library : Simplifies working with LLMs. Datasets : Custom text data for fine-tuning. Hardware : A GPU (cloud platforms like Google Colab are great for this). Let’s proceed with an example using Hugging Face. Step-by-Step Fine-Tuning with Hugging Face Step 1: Install the Required Libraries Install Hugging Face Transformers and Datasets. Step 1: Install Python Ensure you have Python installed (preferably version 3.8 or higher ). Download Python from python.org . Follow installation instructions for your operating syst...

Generative AI and Large Language Models (LLMs)! Part 1: Understanding Generative AI What is Generative AI? Generative AI refers to systems that can create new content—such as text, images, music, or even code—by learning patterns from existing data. Unlike traditional AI models, which are primarily designed for classification or prediction tasks, generative AI focuses on producing something novel and realistic. For example: DALL·E creates images from text prompts. GPT models generate human-like text for conversations, stories, or coding. Core Components of Generative AI : Neural Networks : These are mathematical models inspired by the human brain, capable of processing vast amounts of data to detect patterns. Generative AI often uses deep neural networks. Generative Models : GANs (Generative Adversarial Networks) : Two networks (a generator and a discriminator) work together to create realistic outputs. Transformers : Revolutionized NLP with attention mechanisms and are the backb...