Nvidia Generative AI with LLMs (NCA-GENL) Exam Questions 2025

Nvidia Generative AI with LLMs (NCA-GENL) Exam Questions 2025

SkillCertPro Offerings (Instructor Note) :

• We are offering 230 latest Nvidia Generative AI with LLMs (NCA-GENL) Exam Questions 2025 for practice, which will help you to score higher in your exam.
• Aim for above 85% or above in our mock exams before giving the main exam. 
• Do review wrong & right answers and thoroughly go through the explanations provided to each question which will help you understand the question.
• Master Cheat Sheet was prepared by instructors which contain personal notes of them for all exam objectives. Carefully written to help you all understand the topics easily.
• It is recommended to use the Master Cheat Sheet just before 2-3 days of the main exam to cram the important notes.
• Weekly updates: We have a dedicated team updating our question bank on a regular basis, based on the feedback of students on what appeared on the actual exam, as well as through external benchmarking.

Key Exam Information:

• Purpose:
  • Validates foundational concepts for developing, integrating, and maintaining AI-driven applications using generative AI and large language models (LLMs) with NVIDIA solutions. 
• Target Audience:
  • AI professionals, data scientists, machine learning engineers, and anyone seeking to validate their expertise in generative AI and LLMs, particularly with NVIDIA’s AI technologies.
• Exam Format:
  • Online, proctored exam.
  • Duration: 1 hour.
  • Number of questions: Approximately 50 multiple-choice questions.
  • Language: English.
• Cost:
  • Around $135 (it is best to verify the most current price on the Nvidia website).
• Validity:
  • The certification is valid for two years from issuance.
• Prerequisites:
  • A basic understanding of generative AI and large language models.

Good to have knowledge when attempting Nvidia Generative AI with LLMs (NCA-GENL) Exam Questions 2025:

• Core AI Principles:
  • General deep learning concepts (e.g., neural network basics, activation and loss functions).
  • Transformer architecture (encoding, decoding, attention mechanisms).
• LLM-Specific Knowledge:
  • Natural language processing (NLP) and large language models (LLMs).
  • Prompt engineering.
  • Alignment strategies.
  • Data analysis and visualization.
  • Experimentation methodologies.
  • Data preprocessing and feature engineering.
• NVIDIA-Specific Tools and Optimization:
  • NVIDIA’s infrastructure and AI development services.
  • Memory mapping techniques for machine learning.
  • Utilization of Python libraries for LLMs.
  • Nvidia RAPIDS, CuDF, CuGraph, and other Nvidia accelerated tools.
• Software Development:
  • Coding skills, especially in Python.
  • LLM integration and deployment.

Preparation Tips:

• Understand the exam objectives and format.
• Strengthen your foundational knowledge of machine learning and deep learning.
• Gain familiarity with NVIDIA products and tools.
• Develop strong programming skills, particularly in Python.
• Engage in hands-on projects.