Free Practice Questions for NVIDIA Generative AI LLM Associate Certification
- Guide checked for updates:
- 18 Sep 2026
- Question bank created:
- 2 Jul 2026
- Question bank last updated:
- 11 Aug 2026
Study with 323 exam-style practice questions designed to help you prepare for the NVIDIA Generative AI LLM Associate.
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Exam Details
Key information about NVIDIA Generative AI LLM Associate
- Multiple choice
Associate
generative-ai-llms-nca-genl
Bachelor’s degree in computer science, software engineering, AI, or a related field; knowledge of Python, C, and AI frameworks (PyTorch, TensorFlow); solid understanding of neural networks and deep learning models.
Generative AI-large language model (LLM) associate developers responsible for contributing to the development, programming, and quality assurance of state-of-the-art generative AI LLM systems. This includes working with AI teams to develop datasets, select and train models, implement testing and debugging, understand model deployment, and develop high-quality software.
Exam Topics & Skills Assessed
Skills measured (from the official study guide)
1: Core Machine Learning and AI Knowledge
1.1: Assist in deployment and evaluation of model scalability, performance, and reliability under the supervision of senior team members.
Assist in deployment and evaluation of model scalability, performance, and reliability under the supervision of senior team members.
1.2: Awareness of the process of extracting insights from large datasets using data mining, data visualization, and similar techniques.
Awareness of the process of extracting insights from large datasets using data mining, data visualization, and similar techniques.
1.3: Build LLM use cases such as retrieval-augmented generation (RAG), chatbots, and summarizers.
Build LLM use cases such as retrieval-augmented generation (RAG), chatbots, and summarizers.
1.4: Curate and embed content datasets for RAGs.
Curate and embed content datasets for RAGs.
1.5: Familiarity with the fundamentals of machine learning (e.g., feature engineering, model comparison, cross validation).
Familiarity with the fundamentals of machine learning (e.g., feature engineering, model comparison, cross validation).
1.6: Familiarity with the capabilities of Python natural language packages (spaCy, NumPy, vector databases, etc.).
Familiarity with the capabilities of Python natural language packages (spaCy, NumPy, vector databases, etc.).
1.7: Read research papers (articles, conference papers, etc.) to identify emerging LLM trends and technologies.
Read research papers (articles, conference papers, etc.) to identify emerging LLM trends and technologies.
1.8: Select and use models to create text embeddings.
Select and use models to create text embeddings.
1.9: Use prompt engineering principles to create prompts to achieve desired results.
Use prompt engineering principles to create prompts to achieve desired results.
1.10: Use Python packages (spaCy, NumPy, Keras, etc.) to implement specific traditional machine learning analyses.
Use Python packages (spaCy, NumPy, Keras, etc.) to implement specific traditional machine learning analyses.
2: Data Analysis
2.1: Awareness of the process of extracting insights from large datasets using data mining, data visualization, and similar techniques.
Awareness of the process of extracting insights from large datasets using data mining, data visualization, and similar techniques.
2.2: Compare models using statistical performance metrics, such as loss functions or proportion of explained variance.
Compare models using statistical performance metrics, such as loss functions or proportion of explained variance.
2.3: Conduct data analysis under the supervision of a senior team member.
Conduct data analysis under the supervision of a senior team member.
2.4: Create graphs, charts, or other visualizations to convey the results of data analysis using specialized software.
Create graphs, charts, or other visualizations to convey the results of data analysis using specialized software.
2.5: Identify relationships and trends or any factors that could affect the results of research.
Identify relationships and trends or any factors that could affect the results of research.
3: Experimentation
3.1: Awareness of the process of extracting insights from large datasets using data mining, data visualization, and similar techniques.
Awareness of the process of extracting insights from large datasets using data mining, data visualization, and similar techniques.
3.2: Compare models using statistical performance metrics, such as loss functions or proportion of explained variance.
Compare models using statistical performance metrics, such as loss functions or proportion of explained variance.
3.3: Conduct data analysis under the supervision of a senior team member.
Conduct data analysis under the supervision of a senior team member.
3.4: Create graphs, charts, or other visualizations to convey the results of data analysis using specialized software.
Create graphs, charts, or other visualizations to convey the results of data analysis using specialized software.
3.5: Identify relationships and trends or any factors that could affect the results of research.
Identify relationships and trends or any factors that could affect the results of research.
4: Software Development
4.1: Assist in the deployment and evaluations of model scalability, performance, and reliability under the supervision of senior team member.
Assist in the deployment and evaluations of model scalability, performance, and reliability under the supervision of senior team member.
4.2: Build LLM use cases such as RAGs, chatbots, and summarizers.
Build LLM use cases such as RAGs, chatbots, and summarizers.
4.3: Familiarity with the capabilities of Python natural language packages (spaCy, NumPy, vector databases, etc.).
Familiarity with the capabilities of Python natural language packages (spaCy, NumPy, vector databases, etc.).
4.4: Identify system data, hardware, or software components required to meet user needs.
Identify system data, hardware, or software components required to meet user needs.
4.5: Monitor functioning of data collection, experiments, and other software processes.
Monitor functioning of data collection, experiments, and other software processes.
4.6: Use Python packages (spaCy, NumPy, Keras, etc.) to implement specific traditional machine learning analyses.
Use Python packages (spaCy, NumPy, Keras, etc.) to implement specific traditional machine learning analyses.
4.7: Write software components or scripts under the supervision of a senior team member.
Write software components or scripts under the supervision of a senior team member.
5: Trustworthy AI
5.1: Describe the ethical principles of trustworthy AI.
Describe the ethical principles of trustworthy AI.
5.2: Describe the balance between data privacy and the importance of data consent.
Describe the balance between data privacy and the importance of data consent.
5.3: Describe how to use NVIDIA and other technologies to improve AI trustworthiness.
Describe how to use NVIDIA and other technologies to improve AI trustworthiness.
5.4: Describe how to minimize bias in AI systems.
Describe how to minimize bias in AI systems.
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