Free Practice Questions for NVIDIA Generative AI LLM Associate Certification
Study with 333 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)
Core Machine Learning and AI Knowledge(30%%)
Assist in deployment and evaluation of model scalability, performance, and reliability under the supervision of senior team members.
- model scalability evaluation
- model performance evaluation
- model reliability evaluation
Awareness of the process of extracting insights from large datasets using data mining, data visualization, and similar techniques.
- data mining
- data visualization
- insights extraction
Build LLM use cases such as retrieval-augmented generation (RAG), chatbots, and summarizers.
- RAG implementation
- chatbot development
- summarizer building
Curate and embed content datasets for RAGs.
- content dataset curation
- content dataset embedding
Familiarity with the fundamentals of machine learning (e.g., feature engineering, model comparison, cross validation).
- feature engineering
- model comparison
- cross validation
Familiarity with the capabilities of Python natural language packages (spaCy, NumPy, vector databases, etc.).
- Python NLP packages
- spaCy
- NumPy
- vector databases
Read research papers (articles, conference papers, etc.) to identify emerging LLM trends and technologies.
- LLM trend identification
- technology research
Select and use models to create text embeddings.
- model selection for text embeddings
- text embedding creation
Use prompt engineering principles to create prompts to achieve desired results.
- prompt engineering
- prompt creation
Use Python packages (spaCy, NumPy, Keras, etc.) to implement specific traditional machine learning analyses.
- traditional ML analysis
- Python packages (spaCy, NumPy, Keras)
Data Analysis(14%%)
Awareness of the process of extracting insights from large datasets using data mining, data visualization, and similar techniques.
- data mining
- data visualization
- insights extraction
Compare models using statistical performance metrics, such as loss functions or proportion of explained variance.
- model comparison
- statistical performance metrics
- loss functions
- explained variance
Conduct data analysis under the supervision of a senior team member.
- supervised data analysis
Create graphs, charts, or other visualizations to convey the results of data analysis using specialized software.
- data visualization creation
- graphs
- charts
Identify relationships and trends or any factors that could affect the results of research.
- relationship identification
- trend identification
- factor analysis
Experimentation(22%%)
Awareness of the process of extracting insights from large datasets using data mining, data visualization, and similar techniques.
- data mining
- data visualization
- insights extraction
Compare models using statistical performance metrics, such as loss functions or proportion of explained variance.
- model comparison
- statistical performance metrics
- loss functions
- explained variance
Conduct data analysis under the supervision of a senior team member.
- supervised data analysis
Create graphs, charts, or other visualizations to convey the results of data analysis using specialized software.
- data visualization creation
- graphs
- charts
Identify relationships and trends or any factors that could affect the results of research.
- relationship identification
- trend identification
- factor analysis
Software Development(24%%)
Assist in the deployment and evaluations of model scalability, performance, and reliability under the supervision of senior team member.
- model scalability deployment
- model performance evaluation
- model reliability evaluation
Build LLM use cases such as RAGs, chatbots, and summarizers.
- RAGs building
- chatbots building
- summarizers building
Familiarity with the capabilities of Python natural language packages (spaCy, NumPy, vector databases, etc.).
- Python NLP packages
- spaCy
- NumPy
- vector databases
Identify system data, hardware, or software components required to meet user needs.
- system component identification
- hardware component identification
- software component identification
Monitor functioning of data collection, experiments, and other software processes.
- data collection monitoring
- experiment monitoring
- software process monitoring
Use Python packages (spaCy, NumPy, Keras, etc.) to implement specific traditional machine learning analyses.
- traditional ML analysis
- Python packages (spaCy, NumPy, Keras)
Write software components or scripts under the supervision of a senior team member.
- software component writing
- script writing
Trustworthy AI(10%%)
Describe the ethical principles of trustworthy AI.
- ethical AI principles
Describe the balance between data privacy and the importance of data consent.
- data privacy
- data consent
Describe how to use NVIDIA and other technologies to improve AI trustworthiness.
- NVIDIA technologies for AI trustworthiness
- AI trustworthiness improvement
Describe how to minimize bias in AI systems.
- AI bias minimization
Techniques & products