NVIDIA-Certified Professional: Generative AI LLMs
NCP-GENL
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Your team is deploying a 70B parameter LLM for production inference with strict latency SLAs (<50ms per token). You have H100 GPUs and need to maximize throughput while maintaining quality. Which TensorRT-LLM optimization strategy provides the best balance?
Use INT4 AWQ quantization across all layers with maximum batch size
Use INT8 SmoothQuant for weights only, keep activations in FP16
Deploy with FP16 precision and rely on tensor core optimization alone
Apply FP8 quantization with per-token activation calibration and FP8 KV cache
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Pruebas de Práctica Incluidas en el Paquete (7)
NVIDIA Generative AI LLMs Professional (NCP-GENL) - Practice Exam 1
Practice exam #1 covering Distributed Training & Infrastructure including tensor cores, profiling, and DGX optimization.
NVIDIA Generative AI LLMs Professional (NCP-GENL) - Practice Exam 2
Practice exam #2 emphasizing Model Optimization including quantization, pruning, distillation, and TensorRT-LLM.
NVIDIA Generative AI LLMs Professional (NCP-GENL) - Practice Exam 3
Practice exam #3 focusing on Data Preparation including cleaning, tokenization, and RAPIDS workflows.
NVIDIA Generative AI LLMs Professional (NCP-GENL) - Practice Exam 4
Practice exam #4 covering Prompt Engineering & Advanced Techniques including few-shot, CoT, ReAct, and constrained decoding.
NVIDIA Generative AI LLMs Professional (NCP-GENL) - Practice Exam 5
Practice exam #5 emphasizing Transformer Architecture & Core Concepts including attention mechanisms and sampling strategies.
NVIDIA Generative AI LLMs Professional (NCP-GENL) - Practice Exam 6
Practice exam #6 covering production deployment, safety guardrails, and performance tuning for LLM applications.
NVIDIA Generative AI LLMs Professional (NCP-GENL) - Practice Exam 7
Comprehensive practice exam #7 covering all NCP-GENL domains with scenario-based questions and real-world LLM deployment challenges.
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