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NVIDIA NCA-GENM Exam Overview:
| Certification Vendor: | NVIDIA |
| Exam Name: | NVIDIA Certified Associate - Generative AI Multimodal |
| Exam Number: | NCA-GENM |
| Exam Price: | $125 USD |
| Certificate Validity Period: | 2 years |
| Exam Format: | Multiple-choice |
| Available Languages: | English |
| Real Exam Qty: | 50-60 |
| Exam Duration: | 60 minutes |
| Passing Score: | Not publicly disclosed |
| Related Certifications: | NVIDIA Certified Associate - Generative AI LLMs (NCA-GENL) NVIDIA Certified Associate - AI Infrastructure and Operations (NCA-AIIO) |
| Sample Questions: | NVIDIA NCA-GENM Sample Questions |
| Exam Way: | Online, remotely proctored |
| Pre Condition: | A basic understanding of generative AI and multimodal models. |
| Official Syllabus URL: | https://www.nvidia.com/en-us/learn/certification/generative-ai-multimodal/ |
NVIDIA NCA-GENM Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Performance Optimization | 10% | - Techniques for optimizing AI performance - Monitoring and improving system efficiency |
| Core ML & AI Knowledge | 20% | - Basic concepts and terminology - Key algorithms and techniques |
| Data Analysis & Visualization | 10% | - Visualization techniques for multimodal data - Data preprocessing and feature engineering |
| Experimentation | 25% | - Model evaluation and comparison - A/B testing - Experimental design - Hypothesis testing |
| Software Development & Engineering | 15% | - Integration and deployment of multimodal AI systems - Python libraries for multimodal AI |
| Multimodal Data | 15% | - Handling and integrating text, image, and audio data - Applications and use cases |
| Trustworthy AI | 5% | - Ensuring fairness and transparency - Ethical considerations in AI development |
NVIDIA Generative AI Multimodal Sample Questions:
1. What advantage does multimodal learning have over unimodal learning?
A) It is more reliable than unimodal learning.
B) It can capture more complex patterns and relationships in data.
C) It requires fewer data samples for learning.
D) It is easier to collect multimodal data than unimodal data.
2. You are developing a ML model for image classification. You have a dataset with 10,000 images of cats, dogs and birds. Which of the following ML models would be the most appropriate choice for this task?
A) Logistic Regression
B) Convolutional Neural Network (CNN)
C) Linear Regression
D) K-Means Clustering
3. What is a common method to reduce the computational cost of deep learning models during inference?
A) Increasing the batch size.
B) By replacing activation functions in some neurons with simpler ones.
C) Adding more convolutional filters.
D) Pruning weights or neurons.
4. What does mixed-precision training refer to?
A) Training a model using incomplete or missing information from different modalities.
B) Training a model using diverse data types while addressing challenges related to missing or incomplete information.
C) Training a model using different types of data, such as text, images, audio, time series, and geospatial information.
D) Training a model using multiple precision levels, such as using both single-precision and double- precision floating-point numbers.
5. What is the role of CLIP (Contrastive Language-Image Pretraining) in text-to-image generation?
A) CLIP provides a common embedding space for both the textual and image modalities.
B) CLIP is used to enhance datasets through data augmentation for text-to-image generation.
C) CLIP is used to generate image captions from textual input.
D) CLIP is used to convert textual input into image embeddings.
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: B | Question # 3 Answer: D | Question # 4 Answer: D | Question # 5 Answer: A |
By Troy
