Comprehensive Generative AI Practice Test: Basic to Advanced

Welcome to the Generative AI Mastery Practice Test – your ultimate preparation guide to test and sharpen your knowledge of Generative AI. Whether you're a developer, researcher, or AI enthusiast, this practice test series covers both theoretical and coding-based MCQs, multiple selection, and true/false questions that reflect real-world applications and key concepts of modern Generative AI.

This course is designed to challenge your understanding, reinforce foundational concepts, and help you confidently apply your knowledge in practical scenarios. Each section focuses on high-priority topics and includes questions curated based on their relevance in industry and development work.

Section 1: Foundations of Generative AI

Dive into the building blocks of Generative AI. This section covers basic concepts like supervised vs unsupervised learning, neural networks, transformers, embeddings, and latent space. Understand what makes models like GPT and Diffusion Models powerful, and learn the theoretical groundwork you need to move forward confidently.

Section 2: Key Models and Techniques

Explore the powerhouse models and methods behind Generative AI. This section includes deep dives into VAEs, GANs, Diffusion Models, Transformers, and LLMs. You'll also tackle key training concepts like loss functions and fine-tuning, giving you a strong handle on how these models are built and optimized.

Section 3: Building with Generative AI

Learn how to implement and deploy Generative AI solutions. This hands-on section includes prompt engineering, chaining outputs, API usage (like OpenAI), frameworks such as LangChain and Transformers library, and fine-tuning. Coding-based questions dominate here, offering practical insight into building real-world AI apps.

Section 4: Applications Across Modalities

From text to images, audio to code—Generative AI spans multiple domains. This section covers applications like text generation, code completion, image generation, speech synthesis, and multimodal models like Gemini and GPT-4. It helps you understand how to adapt your skills across creative and technical fields.

Section 5: Safety, Ethics & Governance

Responsible AI matters. This section addresses critical topics like hallucinations, bias, fairness, model alignment, red teaming, and the role of policies and open-source in AI development. You’ll test your understanding of how to balance innovation with ethical and safe deployment.

Section 6: Future Trends and Innovations

Step into the future of Generative AI. This section covers rapidly evolving areas like AI agents, AutoGPT, tool use, personal AI with memory, and autonomous workflows. Understand the direction in which AI is heading and what it means for future applications and careers.