NCA-GENL exam dumps

NCA-GENL practice question 48 of 228

NVIDIA-Certified Associate - Generative AI LLMs. Associate level, NVIDIA. Free question with the correct answer and a full explanation.

NCA-GENL Question 48

Select 4

You are tasked with identifying emerging trends in large language models (LLMs) for a company planning its AI strategy. While reviewing recent research papers, which key topics should you prioritize to stay aligned with the latest advancements in LLM technology?

  1. A

    Techniques for fine-tuning LLMs using smaller datasets

  2. B

    Advancements in hardware acceleration for LLM training and inference

  3. C

    Research on replacing LLMs with rule-based systems for NLP tasks

  4. D

    Emerging methods for improving the energy efficiency of LLMs

  5. E

    Studies on ethical challenges and bias mitigation in LLM deployments

Show answer and explanation

Correct answers: A, B, D, E

Explanation

To identify emerging trends in LLMs, researchers and practitioners focus on areas that improve model efficiency, scalability, and ethical deployment. Fine-tuning techniques, hardware advancements, energy optimization, and bias mitigation are well-documented priorities in recent literature, while replacing LLMs with outdated rule-based systems is not seen as a forward-looking trend.

  • A. Correct.

    Techniques for fine-tuning LLMs using smaller datasets are a key research area as they address the need for domain-specific performance while reducing computational costs.

  • B. Correct.

    Advancements in hardware acceleration are critical for improving training and inference efficiency, which is a major focus for scaling LLMs.

  • C. Incorrect.

    While rule-based systems have niche applications, they are largely outdated for most NLP tasks compared to LLMs, making this an unlikely emerging trend.

  • D. Correct.

    Improving the energy efficiency of LLMs is gaining traction as part of the push toward more sustainable AI practices.

  • E. Correct.

    Ethical challenges and bias mitigation are highly relevant as LLMs are increasingly deployed in sensitive real-world applications.

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