Скачать или смотреть NVIDIA AI Certification (NCA-GENM) Practice Exam Part 7 (Q121-140) | MLOps, Overfitting & Metrics

🚀 Get production-ready with Part 7 of our NCA-GENM exam prep series! This video provides an in-depth review of practice questions 121 through 140, focusing on the real-world challenges of training, evaluating, and deploying high-performance AI models.

This session is a practical guide to the entire ML lifecycle. You'll master how to diagnose and solve one of the most common training problems: overfitting, and learn the distinct purposes of Dropout and Quantization. We'll then dive into essential MLOps topics, including how to manage VRAM with batch sizes, the benefits of asynchronous requests in Triton Inference Server, and how to properly monitor your NVIDIA GPUs with Prometheus. Finally, we'll sharpen your analytical skills by covering critical evaluation metrics, explaining why F1-score is superior to Accuracy for imbalanced data, and how FID is used to judge the quality of generative models.

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🎯 WHO IS THIS FOR?

Anyone preparing for the NVIDIA NCA-GENM Certification exam.
AI developers and students who want to test their knowledge of advanced multimodal concepts.
Professionals looking for a practical, question-based review of generative AI applications.

👍 If you find this video helpful, please Like, Share, and Subscribe for the next parts in this series! Drop any questions you have in the comments below—let's learn together!

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