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NVIDIA NCA-GENM Exam Overview:
| Certification Vendor: | NVIDIA |
|---|---|
| Exam Name: | Generative AI Multimodal Certification Exam |
| Exam Number: | NCA-GENM |
| Exam Format: | Multiple choice |
| Exam Price: | $125 USD |
| Certificate Validity Period: | 2 years |
| Exam Duration: | 60 minutes |
| Real Exam Qty: | 50-60 |
| Available Languages: | English, Chinese |
| Related Certifications: | NVIDIA-Certified Associate: Generative AI LLMs (NCA-GENL) |
| Passing Score: | Not publicly disclosed |
| Recommended Training: | Fundamentals of Generative AI Getting Started With Deep Learning |
| Exam Registration: | NVIDIA Certification Portal |
| Sample Questions: | NVIDIA NCA-GENM Sample Questions |
| Exam Way: | Online, remotely proctored |
| Pre Condition: | Basic understanding of generative AI concepts and principles |
| Official Syllabus URL: | https://www.nvidia.com/en-us/learn/certification/generative-ai-multimodal-associate/ |
NVIDIA NCA-GENM Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Performance Optimization | 10% | - Model efficiency and inference optimization - Scalability and deployment considerations - Hardware acceleration with NVIDIA platforms |
| Topic 2: Multimodal Data | 15% | - Data preprocessing, fusion, and representation - Multimodal model architectures and integration - Characteristics of text, image, and audio data |
| Topic 3: Data Analysis and Visualization | 10% | - Interpretation of generative AI outputs - Analyzing multimodal datasets and outputs - Visualization techniques for model behavior and results |
| Topic 4: Trustworthy AI | 5% | - Reliability, fairness, and safety in generative systems - Robustness and error mitigation - Ethical considerations and responsible use |
| Topic 5: Experimentation | 25% | - Experiment design and methodology - Model training, fine-tuning, and evaluation - Metrics and validation strategies for generative models |
| Topic 6: Core Machine Learning and AI Knowledge | 20% | - Generative AI principles and techniques - Fundamental concepts of machine learning and deep learning - Neural network architectures relevant to multimodal systems |
| Topic 7: Software Development and Engineering | 15% | - Libraries, frameworks, and tools for multimodal AI - Best practices for building and maintaining systems - Development workflows for generative AI applications |
NVIDIA Generative AI Multimodal Sample Questions:
In LLM evaluation, what does "zero-shot learning" refer to?
- A. The model's ability to perform tasks it has not been explicitly trained on
- B. The model's ability to learn from zero examples
- C. A technique to reduce training time to zero
- D. The model's performance after extensive training
Correct Answer: A 🗳️
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Which of the following is a component of the Content Authenticity Initiative?
- A. Ethical AI development
- B. Content credential
- C. Data encryption
- D. Content validity
Correct Answer: B 🗳️
Explanation: Only visible for SurePassExams members. You can sign-up / login (it's free).
Which metric is commonly used to evaluate machine-translation models?
- A. Accuracy
- B. Mean Absolute Error (MAE)
- C. BLEU score
- D. F1 score
Correct Answer: C 🗳️
Explanation: Only visible for SurePassExams members. You can sign-up / login (it's free).
What is the purpose of a kernel in a Convolutional Neural Network (CNN)?
- A. To calculate the loss function.
- B. To classify the data into different categories.
- C. To perform convolution operations on input data.
- D. To normalize the input data.
Correct Answer: C 🗳️
Explanation: Only visible for SurePassExams members. You can sign-up / login (it's free).
Which of the following best describes the role of machine learning in handling multimodal data?
- A. To eliminate the need for human intervention in data analysis.
- B. To enable models to learn from and interpret diverse data types.
- C. To focus on textual data analysis.
- D. To reduce the amount of data needed for accurate predictions.
Correct Answer: B 🗳️
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