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

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

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

The NCA Generative AI LLMs Certification is an entry-level credential designed to validate your understanding of fundamental concepts related to developing, integrating, and maintaining AI-driven applications using generative AI and large language models (LLMs) with NVIDIA solutions. This online, remotely proctored exam consists of 50 questions and has a 60-minute time limit.

Exam Details

• Exam Duration: 1 hour
  
• Certification Level: Associate
• Total Questions: 50
• Exam Prerequisites: Basic understanding of generative AI and large language models
• Exam Language: English
• Exam Validity: 2 years from issuance, with recertification available by retaking the exam
• Exam Credentials: Digital badge and optional certificate upon passing

Who should take the Exam?

• AI DevOps engineers
  
• AI strategists
• Applied data scientists
• Applied data research engineers
• Applied deep learning research scientists
• Cloud solution architects
• Data scientists
• Deep learning performance engineers
• Generative AI specialists
• LLM specialists and researchers
• Machine learning engineers
• Senior researchers
• Software engineers
• Solutions architects

Job Role

A Generative AI-Large Language Model (LLM) Associate Developer contributes to the development, programming, and quality assurance of advanced generative AI LLM systems. Working alongside a team of skilled AI professionals, the associate developer helps in dataset development, model selection, training, and model testing and debugging. Additionally, they play a role in deploying models for applications and are responsible for creating high-quality software designs, programming across various languages and platforms, and maintaining system updates.

Job Responsibilities

• Collaborate with the AI development team to design, code, test, debug, and document programming applications.
  
• Perform system analysis to ensure software and systems meet required specifications.
• Aid in integrating new AI language models into existing systems or creating new ones as needed.
• Assist in the assessment and resolution of application and system performance issues.
• Stay updated on new AI models and developments related to language learning models.
• Contribute to the production of technical documents and manuals.
• Conduct software programming and documentation development under the direction of senior staff.
• Perform prompt engineering.
• Assist in the process of model selection.
• Define, curate, label, and annotate LLM datasets.
• Experiment with A/B testing, evaluate prompts, evaluate models, and produce POCs.

Required Qualifications and Experience

• Bachelor’s degree in computer science, software engineering, AI, or a related field
  
• Knowledge of Python, C, and AI frameworks (PyTorch, TensorFlow, etc.)
• Solid understanding of neural networks and deep learning models

Skills Acquired 

The NCA Generative AI Certification course equips you with a variety of skills, including:

• Fundamentals of Machine Learning and Neural Networks
  
• Prompt Engineering
• Alignment
• Data Analysis and Visualization
• Experimentation
• Data Preprocessing and Feature Engineering
• Experiment Design
• Software Development
• Python Libraries for LLMs
• LLM Integration and Deployment

Exam Prerequisites 

There are no formal prerequisites for enrolling in the NCA Generative AI LLMs Certification Course. However, having prior knowledge in the following areas can be beneficial:

• A basic understanding of Generative AI and Large Language Models (LLMs)
• Basic familiarity with Python and Linux

Knowledge Acquired

By enrolling in the NCA Generative AI LLMs certification course, you will:

• Gain fundamental knowledge, hands-on skills, and a practical understanding of how generative AI works.
  
• Explore the latest advancements in generative AI research to understand how businesses utilize cutting-edge technology to create value.
• Receive guidance from experienced AI practitioners who are actively engaged in developing and deploying AI solutions for real-world business applications.

Course Outline

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

Domain 1: Fundamental Machine Learning and AI Proficiency (30%)

• 1.1 Support the implementation and evaluation of model scalability, performance, and dependability under the guidance of senior team members.
• 1.2 Understand methods for extracting insights from extensive datasets using data mining, data visualization, and related techniques.
• 1.3 Develop use cases for large language models (LLMs), such as retrieval-augmented generation (RAG), chatbots, and summarizers.
• 1.4 Collect and integrate content datasets for retrieval-augmented generation (RAG).
• 1.5 Grasp the basics of machine learning, including feature engineering, model evaluation, and cross-validation techniques.
• 1.6 Have knowledge of Python libraries for natural language processing, such as spaCy, NumPy, and vector databases.
• 1.7 Analyze research papers and conference articles to identify new trends and technologies in large language models.
• 1.8 Choose and apply models to generate text embeddings.
• 1.9 Utilize prompt engineering techniques to design effective prompts for desired outcomes.
• 1.10 Implement traditional machine learning analyses using Python libraries like spaCy, NumPy, and Keras.

Domain 2: Understanding Data Analytics (14%)

• 2.1 Understand techniques for extracting insights from large datasets using methods such as data mining and data visualization.
• 2.2 Compare different models using statistical performance metrics like loss functions or the proportion of explained variance.
• 2.3 Perform data analysis under the direction of senior team members.
• 2.4 Create visual representations, including graphs and charts, to communicate the results of data analyses using specialized tools.
• 2.5 Identify trends, relationships, and factors that could influence research results.

Domain 3: Overview of Experimentation and Analysis (22%)

• 3.1 Understand techniques for extracting insights from large datasets using data mining, data visualization, and related methods.
• 3.2 Learn to compare different models using statistical performance metrics like loss functions or the proportion of explained variance.
• 3.3 Conduct data analysis under the direction of senior team members.
• 3.4 Create visual representations, including graphs and charts, to communicate the results of data analyses using specialized tools.
• 3.5 Identify trends, relationships, and factors that could influence research results.

Domain 4: Fundamentals of Software Development and Maintenance (24%)

• 4.1 Support the deployment and assessment of model scalability, performance, and reliability under the guidance of senior team members.
• 4.2 Develop use cases for large language models (LLMs), such as retrieval-augmented generation (RAG), chatbots, and summarizers.
• 4.3 Be familiar with the capabilities of Python libraries for natural language processing, such as spaCy, NumPy, and vector databases.
• 4.4 Determine the data, hardware, or software components needed to meet user requirements.
• 4.5 Monitor data collection processes, experiments, and other software operations.
• 4.6 Implement traditional machine learning analyses using Python libraries like spaCy, NumPy, and Keras.
• 4.7 Write software components or scripts under the supervision of senior team members.

Domain 5: Understanding Ethical and Trustworthy AI (10%)

• 5.1 Explain the ethical principles underlying trustworthy AI.
• 5.2 Balance data privacy concerns with the necessity of data consent.
• 5.3 Use NVIDIA and other technologies to enhance the trustworthiness of AI systems.
• 5.4 Implement strategies to minimize bias in AI systems.

What do we offer?

• Full-Length Mock Test with unique questions in each test set
• Practice objective questions with section-wise scores
• In-depth and exhaustive explanation for every question
• Reliable exam reports evaluating strengths and weaknesses
• Latest Questions with an updated version
• Tips & Tricks to crack the test
• Unlimited access

What are our Practice Exams?

• Practice exams have been designed by professionals and domain experts that simulate real-time exam scenario.
• Practice exam questions have been created on the basis of content outlined in the official documentation.
• Each set in the practice exam contains unique questions built with the intent to provide real-time experience to the candidates as well as gain more confidence during exam preparation.
• Practice exams help to self-evaluate against the exam content and work towards building strength to clear the exam.
• You can also create your own practice exam based on your choice and preference