NVIDIA-Certified Professional: Generative AI LLMs
NCP-GENL
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When deploying a 70B parameter LLM for production inference with strict latency requirements (<100ms) and limited GPU memory, which TensorRT-LLM optimization technique provides the best balance of speed and model quality?
Deploy the full FP32 model and rely on batch processing to amortize latency
Implement model pruning to remove 50% of parameters before deployment
Use FP8 quantization with speculative decoding to balance quality and latency
Apply aggressive INT4 post-training quantization (PTQ) to all layers
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Practice Tests Included in the Bundle (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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