Generative AI Engineering: Master Mock Interviews
Ace your AI engineering interviews with real-world scenarios on RAG, LangChain, Fine-Tuning, and LLM Deployment.
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The title "AI Engineer" has become the most sought-after role in the tech industry, but building enterprise-grade Generative AI applications is incredibly difficult. Prototyping a chatbot in a Jupyter notebook is easy; deploying it to millions of users without memory bottlenecks, prompt injections, or massive hallucinations requires a deep understanding of architecture. The Generative AI Engineering: Master Mock Interviews course is designed to test whether you have what it takes to build AI in production.
This comprehensive test bank throws you directly into the trenches of modern AI development. Across four distinct, randomized exam sets, you will face 200 scenario-based engineering challenges. First, you will tackle Information Retrieval (RAG), solving issues like the "Lost in the Middle" phenomenon and optimizing dense vector searches. Next, you will test your Prompt Engineering skills, orchestrating autonomous LangChain agents and preventing adversarial jailbreaks.
The exams get progressively harder as you move to the model layer. You will be tested on your ability to fine-tune 70B parameter open-source models using QLoRA on consumer hardware, and applying RLHF for safety alignment. Finally, you will face the ultimate MLOps gauntlet. You will answer complex questions on optimizing the KV Cache with PagedAttention, streaming token responses via Server-Sent Events (SSE), and deploying quantized models to edge devices. By the end of these exams, you will be battle-tested and ready to architect the future of AI.
Basic Info:
Course locale: English (India)
Course instructional level: Intermediate to Advanced
Course category: IT & Software
Course subcategory: Artificial Intelligence
Learning Objectives
🔹Evaluate architectural strategies for Retrieval-Augmented Generation (RAG), including Vector DB filtering and Re-ranking models.🔹Test your ability to build autonomous LLM Agents using ReAct prompting, Function Calling, and Chain-of-Thought (CoT).
🔹Assess your proficiency in model alignment, solving catastrophic forgetting, and executing PEFT/QLoRA fine-tuning.
🔹Validate your MLOps expertise by optimizing LLM deployment with GGUF Quantization, vLLM, and PagedAttention.
Prerequisites
🔹A strong foundation in Python and backend software engineering. Familiarity with the concepts of Large Language Models (LLMs) and OpenAI APIs. A desire to pass rigorous technical interviews for specialized "AI Engineer" roles.Who This Course Is For
🔹Software Engineers transitioning into GenAI application development (LangChain, LlamaIndex). Machine Learning Engineers looking to master LLM deployment, fine-tuning, and quantization. Solutions Architects needing to understand how to build secure, hallucination-free enterprise AI systems.
Course Details
| Price | FREE |
| Views | 0 |
| Lectures | 0 |
| Duration | 200 questions |
| Last Update | 28-Jun-2026 |
| Release Date | 28-Jun-2026 |
| Category | IT & Software |
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This course includes:
📹 Video lectures
📄 Downloadable resources
📱 Mobile & desktop access
🎓 Certificate of completion
♾️ Lifetime access
