Skip to main content

Theory Round Preparation

Goal of this file: Provide a concise but deep checklist of theory topics and sample questions tailored for backend SDE-II roles.

1. Operating System Fundamentals​

  • Key Concepts

    • Processes vs Threads
      • Process: independent execution with its own address space.
      • Thread: lightweight unit of execution within a process, shared memory.
    • Context Switching
      • When and why it happens; cost of switching.
    • Scheduling
      • Preemptive vs cooperative multitasking.
      • Common algorithms: Round Robin, Priority scheduling.
    • Memory Management
      • Stack vs heap, virtual memory, paging, segmentation (high-level).

  • Sample Questions

    • Q1: What’s the difference between a process and a thread? Why are threads useful for servers?
    • Q2: What is context switching? Why is it not free?
    • Q3: Explain how virtual memory works at a high level.

2. Networking Fundamentals​

  • Key Concepts

    • OSI vs TCP/IP Model (high-level)
    • TCP vs UDP
      • TCP: reliable, ordered, connection-oriented.
      • UDP: connectionless, best-effort, no ordering.
    • HTTP Basics
      • Methods (GET, POST, PUT, DELETE, PATCH).
      • Status codes (2xx, 3xx, 4xx, 5xx).
      • Idempotency and safe methods.
    • DNS
      • Domain resolution, caching.
    • Proxies & Load Balancers
      • Reverse proxy vs forward proxy.
      • Basic load balancing strategies: round robin, least connections.
    • TLS / HTTPS
      • Encryption, certificates (conceptual).

  • Sample Questions

    • Q1: How does a browser reach https://example.com? Walk through DNS, TCP, TLS, HTTP.
    • Q2: When would you use UDP instead of TCP?
    • Q3: What is a reverse proxy? How is it used in web architectures?

3. DBMS Theory​

  • Key Concepts

    • Relational Model
      • Tables, rows, columns, primary/foreign keys.
    • Normalization
      • 1NF, 2NF, 3NF basics; when to denormalize.
    • Indexing
      • B-tree indexes, clustered vs non-clustered (high-level).
      • How indexes affect reads vs writes.
    • Transactions & ACID
      • Atomicity, Consistency, Isolation, Durability.
      • Isolation levels: Read Uncommitted, Read Committed, Repeatable Read, Serializable.
    • Locks & Concurrency
      • Shared vs exclusive locks.
      • Deadlocks (high-level).

  • Sample Questions

    • Q1: What is an index? How can it speed up queries and when can it hurt?
    • Q2: Explain the ACID properties with examples.
    • Q3: What is a transaction isolation level? Give an example of a phenomenon (dirty read, phantom read).

4. CAP, ACID, BASE, Consistency Models​

  • CAP Theorem

    • In a distributed system, you can only choose 2 out of 3:
      • Consistency (C): all nodes see the same data at the same time.
      • Availability (A): every request gets a response (success or fail).
      • Partition Tolerance (P): system continues to operate despite network splits.
    • In practice, P is a given for distributed systems; you decide C vs A during partitions.

  • ACID vs BASE

    • ACID (typical relational DBs)
      • Strong guarantees, transactions, strict consistency.
    • BASE
      • Basically Available, Soft-state, Eventual consistency.
      • Used in many NoSQL / distributed systems.

  • Consistency Models

    • Strong Consistency
      • After a write, all subsequent reads see the latest value.
    • Eventual Consistency
      • Reads may see stale values for a while, but eventually converge.
    • Read-Your-Own-Writes
      • A client always sees its own writes.
    • Causal Consistency
      • Preserves cause-effect ordering of operations.

  • Sample Questions

    • Q1: Explain the CAP theorem with an example of a system that favors availability over consistency.
    • Q2: What is eventual consistency? Where is it acceptable or not acceptable?
    • Q3: Compare ACID and BASE. When would you choose one over the other?

5. Putting Theory into System Design Context​

  • Relating OS & Networking

    • OS scheduling and threads influence throughput and latency of backend services.
    • Networking concepts (TCP, HTTP, TLS) define how clients talk to your APIs.

  • Relating DBMS & CAP/Consistency

    • DB choices affect consistency, availability, latency, and developer complexity.
    • For example:
User profile service:
  - Strong consistency needed for password changes (ACID).
  - Eventual consistency acceptable for analytics counters (BASE).
  • How to Answer in Interviews
    • Start with a simple explanation, then add details as the interviewer digs deeper.
    • Always try to connect theory to real systems you might design or build.