Interview Preparator

A clear path to your next interview.

Choose a learning path, study one topic at a time and practice explaining your decisions. Pick up where you left off.

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AI curriculum reviewed: October 30, 2026

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Learning path 03

Agentic Engineering & Delivery

Design bounded agents, then deliver a spec-driven change with review evidence.

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Agents vs Workflows

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  1. 1.Agents vs Workflows
  2. 2.Bounded Agent Loop
  3. 3.Tool Design & Permissions
  4. 4.Context, Memory & Run State
  5. 5.MCP Fundamentals & Version Boundaries
  6. 6.Agent Evaluation & Reliability
  7. 7.Design an Agent Execution Platform
  8. 8.Agentic SDLC: Lifecycle & Roles
  9. 9.Spec-Driven Development
  10. 10.Repository Context & Trust Boundaries
  11. 11.Implementation & Evidence-Based Verification
  12. 12.Review, Release Gates & Provenance
  13. 13.Delivery Case: RAG Source Selection
  14. 14.AI Engineering Interview Questions

Learning path 04

Secure & Reliable AI

Start with threat/failure models, then verify evidence, contain tool effects and inspect release risk.

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AI Threat Modeling & Trust Boundaries

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  1. 1.AI Threat Modeling & Trust Boundaries
  2. 2.Hallucinations & Failure Taxonomy
  3. 3.Reliability Evaluation & Metrics
  4. 4.Grounding & Claim-Level Verification
  5. 5.Retrieval Failure Analysis
  6. 6.Abstention & Uncertainty
  7. 7.Verification Tools & Structured Answers
  8. 8.Direct & Indirect Prompt Injection
  9. 9.Sensitive Data & Exfiltration
  10. 10.Secure Output Handling
  11. 11.Secure RAG: Access & Ingestion
  12. 12.Agent Tool Authorization & Approval
  13. 13.Resource, Cost & Availability Abuse
  14. 14.AI Security Evaluation & Release Gates
  15. 15.Model, Tool & Dependency Supply Chain
  16. 16.Memory & Context Poisoning
  17. 17.MCP & Connector Security
  18. 18.Sandbox, Egress & Execution Isolation
  19. 19.Monitoring & AI Incident Response
  20. 20.Agent-Assisted SDLC Security
  21. 21.Conflicting & Stale Evidence
  22. 22.LLM Judge Calibration & Bias
  23. 23.Long Context & Multilingual Reliability
  24. 24.Reliability User Experience

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AI & Agent Engineering

Artificial Intelligence

Learn AI, machine learning, neural networks and the foundations of language models.

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Generative AI

Build reliable LLM applications: prompts, context, structured outputs, retrieval, tools and evaluation. Follow the eight lessons in order or start with the concept you need.

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Spring AI

Build Java AI applications with ChatClient, structured output, tools, conversation memory and a tested pgvector RAG laboratory.

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Agentic AI

Design bounded agents: workflows, execution loops, permissions, durable state, MCP version boundaries and reliability evaluation.

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Agentic SDLC

Five core lessons and one delivery case: define specs, prepare repository context, verify changes and preserve independent review and release boundaries.

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AI Security

Protect application trust boundaries, authorized data and tool effects with tested policies and explicit limits.

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AI Reliability & Hallucinations

Diagnose retrieval and generation failures, verify evidence and measure the tradeoff between answers and abstention.

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Java & Backend

Java

Build a foundation in Java, OOP, collections, exceptions and concurrency.

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Spring & Spring Boot

Learn dependency injection, Boot configuration, validated REST APIs and security before integrating AI models.

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SOLID Principles

Apply five design principles through good and bad Java examples.

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Design Patterns

Understand the problems, trade-offs and implementation of software design patterns.

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Testing

Learn testing fundamentals, JUnit Jupiter APIs and mocking.

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DSA

Coding Patterns

Recognize problem structures and practice reusable interview solution patterns.

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Data Structures

Compare arrays, linked lists, trees, graphs, heaps, hash tables and tries.

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Algorithms

Explore algorithm families and work through the detailed Quick Sort guide.

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Architecture & Interviews

System Design

Practice scalability, reliability and architectural trade-offs through system design cases.

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Behavioral Questions

Structure concrete examples of collaboration, leadership and problem solving with STAR.

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Common Interview Questions

Practice explaining Java, OOP, algorithms and system design decisions.

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System Design Questions

Practice thirteen system design interview problems, including five AI platform design cases.

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