System Design with AI Interview Guide Designing Scalable, Agentic, and Defensible Systems (Rohit Bhardwaj) (z-library.sk, 1lib.sk, z-lib.sk)

【One-Line Pitch】 A structured, interview-oriented guide to designing scalable and AI-augmented systems, pairing core architecture fundamentals with agentic design patterns and real-world case studies. Best for developers and architects preparing for system design interviews who want to defend their choices under pressure. 【Book Arc】 - **Opening (~0%–15%)**: Establishes the book's three-part structure and its central premise — that modern system design interviews now expect AI-aware thinking alongside classic scalability and performance reasoning. Sets expectations for methodology before mechanics. - **Early (~15%–35%)**: Builds the foundational methodology: how to analyze a design challenge, apply domain-driven strategies, and incorporate AI agents to make a system "intelligent" rather than merely distributed. - **Middle (~35%–65%)**: Moves into technical fundamentals — data modeling, API design, microservices, high availability, and security — with attention to scaling AI workloads and optimizing cloud storage. - **Late (~65%–85%)**: Shifts from components to resilience: fault tolerance, high availability trade-offs, and the security posture needed when AI services sit inside the request path. - **Ending (~85%–100%)**: Case-study driven — ride-sharing, e-commerce, fraud detection, and payment processing — used to rehearse how principles translate into interview-ready answers. 【Key Takeaways】 - **The book is organized as a three-part progression** (Early): methodology first, then technical fundamentals, then applied case studies — a deliberate fundamentals-to-practice arc rather than a question bank. - **AI is treated as a design concern, not a bolt-on** (Early): the excerpts emphasize incorporating AI agents to enhance system intelligence, which reframes "where does the model live?" as an architectural decision. - **Scalability and performance optimization are the baseline vocabulary** (Early): the guide assumes you must reason about growth and latency before you can layer AI on top. - **Domain-driven strategies shape the design conversation** (Early): understanding the problem domain is positioned as the step that precedes component selection. - **Technical fundamentals span data, APIs, and services** (Middle): data modeling, API design, and microservices are covered together, implying they should be reasoned about as one connected surface. - **Resilience and security are first-class topics** (Middle–Late): high availability, fault tolerance, and security are grouped with scaling AI workloads, suggesting AI systems inherit — and intensify — classic reliability obligations. - **Case studies are the practice layer** (Ending): ride-sharing, e-commerce, fraud detection, and payment processing serve as rehearsal grounds for structuring and defending answers aloud. - **The stated audience is interview candidates and architects** (Early): the book targets developers preparing for system design interviews and architects wanting a structured approach to scalable, resilient, AI-aware applications. 【Reading Tips】 - **Read Part 1 slowly, skim Part 2 selectively.** The methodology section is where the book's distinctive angle lives; if you already know microservices and API design, use the middle as a checklist rather than a tutorial. - **Treat the case studies as mock interviews.** Cover the solution, sketch your own design for ride-sharing or payments, then compare — the value is in the articulation, not the reading. - **Prepare a defense, not just a design.** The book's framing stresses "defensible" systems; practice justifying each trade-off (consistency vs. availability, AI latency vs. accuracy) out loud. - **Watch for the AI-scaling thread.** When AI workloads appear alongside cloud storage and fault tolerance, note how they change capacity planning and failure modes — this is likely where interviewers probe. - **Keep a one-page cheat sheet.** Distill domain analysis → data/API/service choices → resilience/security → AI integration into a repeatable interview sequence. 【Coverage Limits】 These excerpts come from the book's front matter and audience description only; they do not cover specific chapters, diagrams, code, or the internal content of the case studies. Claims about depth, examples, and technical specifics beyond the stated three-part structure cannot be verified from the available material.