NVIDIA-Certified Associate: Generative AI and LLMs Certification (NCA-GENL) Practice Exam

NVIDIA - Certified Associate: Generative AI and LLMs Certification (NCA-GENL) Practice Exam

The NVIDIA - Certified Associate: Generative AI and LLMs Certification (NCA-GENL) is a professional certification designed to validate the foundational knowledge and skills needed for working with generative artificial intelligence (AI) models and large language models (LLMs). The certification includes the basics of Generative AI, LLMs working, deployment techniques, and fine-tuning models for real-world applications. It is suitable for individuals interested in understanding and applying Generative AI, in domains like NLP (natural language processing) and AI-driven content generation.
Why is NVIDIA - Certified Associate: Generative AI and LLMs Certification (NCA-GENL) important?

• Certifies your skills in generative AI and LLMs.
• Shows your knowledge of applying LLMs (large language models) function.
• Boosts employability in the growing AI field, with companies using NLP and AI content generation.
• Gives candidates with hands-on knowledge of deploying and fine-tuning AI models.
• Globally recognized certificate from NVIDIA, a leader in AI hardware and software technologies.

Who should take the NVIDIA - Certified Associate: Generative AI and LLMs Certification (NCA-GENL) Exam?

• AI/ML Engineer
• Data Scientist
• NLP Engineer
• AI Developer
• Machine Learning Engineer
• AI Research Scientist
• Business Intelligence Analyst

Skills Evaluated

Candidates taking the certification exam on the NVIDIA - Certified Associate: Generative AI and LLMs Certification (NCA-GENL) is evaluated for the following skills:

• Understanding the fundamentals of generative AI and LLMs.
• Ability to explain the architecture and working principles of LLMs.
• Knowledge of the deployment processes for generative AI models.
• Techniques for fine-tuning and optimizing LLMs for various tasks.
• Practical experience with AI tools, such as NVIDIA’s frameworks for building and deploying AI models.

NVIDIA - Certified Associate: Generative AI and LLMs Certification (NCA-GENL) Certification Course Outline
The NVIDIA - Certified Associate: Generative AI and LLMs Certification (NCA-GENL) Certification covers the following topics -

Module 1. Core Machine Learning and AI Knowledge

• Assist in deployment and evaluation of model scalability, performance, and reliability under the supervision of senior team members.

• Awareness of the process of extracting insights from large datasets using data mining, data visualization, and similar techniques.

• Build LLM use cases such as retrieval-augmented generation (RAG), chatbots, and summarizers.

• Curate and embed content datasets for RAGs.

• Familiarity with the fundamentals of machine learning (e.g., feature engineering, model comparison, cross validation).

• Familiarity with the capabilities of Python natural language packages (spaCy, NumPy, vector databases, etc.).

• Read research papers (articles, conference papers, etc.) to identify emerging LLM trends and technologies.

• Select and use models to create text embeddings.

• Use prompt engineering principles to create prompts to achieve desired results.

• Use Python packages (spaCy, NumPy, Keras, etc.) to implement specific traditional machine learning analyses.

Module 2. Data Analysis

• Awareness of the process of extracting insights from large datasets using data mining, data visualization, and similar techniques.

• Compare models using statistical performance metrics, such as loss functions or proportion of explained variance.

• Conduct data analysis under the supervision of a senior team member.

• Developing graphs, charts, or other visualizations development to convey the results of data analysis using specialized software.

• Identify relationships and trends or any factors that could affect the results of research.

Module 3. Experimentation

• Awareness of the process of extracting insights from large datasets using data mining, data visualization, and similar techniques.

• Compare models using statistical performance metrics, such as loss functions or proportion of explained variance.

• Conduct data analysis under the supervision of a senior team member.

• Developing graphs, charts, or other visualizations to convey the results of data analysis using specialized software.

• Identifying relationships and trends or any factors that could affect the results of research.

Module 4. Software Development

• Help in the deployment and evaluations of model scalability, performance, and reliability under the supervision of senior team member.

• Developing LLM use cases such as RAGs, chatbots, and summarizers.

• Aware of the capabilities of Python natural language packages (spaCy, NumPy, vector databases, etc.).

• Identifying system data, hardware, or software components required for meeting user needs.

• Monitoring functioning of data collection, experiments, and other software processes.

• Applying Python packages (spaCy, NumPy, Keras, etc.) for implementing specific traditional machine learning analyses.

• Write software components or scripts under the supervision of a senior team member.

Module 5. Trustworthy AI

• The ethical principles of trustworthy AI.

• Balancing data privacy and the relevance of data consent.

• Use NVIDIA and other technologies to improve AI trustworthiness.

• Minimize bias in AI systems.