Additional Material

1. Study Material[PDF] : Before you begin, download the 200-page PDF file (Newly Released) provided under the Resources link in the first question of the first practice paper. This latest, updated document contains essential reference material you will need throughout the course to maximize your learning and ensure you are preparing with the most current certification content.

2. Interview Questions & Answers [PDF]: Before you begin, download the 200-page PDF file (Newly Released) provided under the Resources link in the first question of the first practice paper. This latest, updated document contains essential reference material you will need throughout the course to maximize your learning and ensure you are preparing with the most current certification content.

3. Interview Questions & Answers [Audio Book : 2 Hr 30 Mins]: Before you begin, download the 200-page PDF file (Newly Released) provided under the Resources link in the first question of the first practice paper. This latest, updated document contains essential reference material you will need throughout the course to maximize your learning and ensure you are preparing with the most current certification content.

Disclaimer: “This course is not affiliated with or endorsed by NVIDIA. NVIDIA and CUDA are trademarks of NVIDIA Corporation.”

Prepare with confidence for the Certified Associate Generative AI Multimodal (NCA-GENM) certification through this complete, exam-focused practice test and preparation course. This entry-level certification validates your foundational skills in multimodal generative AI—covering text, image, and audio processing—and is ideal for professionals aiming to design, implement, and manage cutting-edge AI systems.

This course is built for AI professionals, developers, and aspiring certification candidates who want both exam readiness and practical, real-world skills. You’ll gain hands-on exposure to critical topics, including:

• Core machine learning and AI knowledge

• Data analysis and visualization for multimodal datasets

• Experimentation, A/B testing, and model evaluation

• Multimodal data preprocessing, labeling, and annotation (text, image, audio)

• Prompt engineering and model selection

• Performance optimization techniques

• Software development and trustworthy AI principles

Our practice tests are carefully crafted to replicate the real certification format—50 multiple-choice questions with a 60-minute limit—giving you an authentic exam experience. Each question includes clear, in-depth explanations, so you not only know the correct answer but also fully understand the reasoning behind it.

Whether you are an AI/ML engineer, data scientist, cloud solution architect, software engineer, or a generative AI specialist, this course will help you bridge knowledge gaps, reinforce your understanding, and confidently approach the exam.

By the end of this course, you will have:

• Mastered the skills needed to pass the NCA-GENM exam

• Built confidence through realistic practice tests

• Learned to apply multimodal AI concepts in practical scenarios

• Strengthened your ability to design, implement, and optimize AI systems

Join now and take your first step toward becoming a certified Generative AI Multimodal professional, ready to excel in one of the fastest-growing fields in AI.

Who this course is for:

• AI/ML engineers and developers working with multimodal models
• AI DevOps/MLOps engineers responsible for productionizing and monitoring generative AI systems
• Applied data scientists and research engineers conducting model evaluation and POCs
• Cloud and solutions architects designing scalable AI integrations
• Generative AI specialists, LLM/multimodal researchers, and deep learning performance engineers
• Software engineers collaborating on AI projects or embedding multimodal intelligence
• AI strategists and technical product managers making informed decisions about model adoption
• Certification seekers preparing for the NCA-GENM exam
• Technical professionals transitioning into AI who have basic programming and ML familiarity