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Real NVIDIA NCA-GENM practice exam questions for easy pass!
Updated: Sep 08, 2026
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NVIDIA NCA-GENM Exam Overview:
| Certification Vendor: | NVIDIA |
|---|---|
| Exam Name: | Generative AI Multimodal Certification Exam |
| Exam Number: | NCA-GENM |
| Available Languages: | English, Chinese |
| Real Exam Qty: | 50-60 |
| Related Certifications: | NVIDIA-Certified Associate: Generative AI LLMs (NCA-GENL) |
| Passing Score: | Not publicly disclosed |
| Exam Price: | $125 USD |
| Exam Duration: | 60 minutes |
| Certificate Validity Period: | 2 years |
| Exam Format: | Multiple choice |
| Recommended Training: | Getting Started With Deep Learning Fundamentals of Generative AI |
| 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 |
|---|---|---|
| Trustworthy AI | 5% | - Robustness and error mitigation - Reliability, fairness, and safety in generative systems - Ethical considerations and responsible use |
| Experimentation | 25% | - Experiment design and methodology - Metrics and validation strategies for generative models - Model training, fine-tuning, and evaluation |
| Core Machine Learning and AI Knowledge | 20% | - Fundamental concepts of machine learning and deep learning - Generative AI principles and techniques - Neural network architectures relevant to multimodal systems |
| Multimodal Data | 15% | - Multimodal model architectures and integration - Characteristics of text, image, and audio data - Data preprocessing, fusion, and representation |
| Performance Optimization | 10% | - Model efficiency and inference optimization - Scalability and deployment considerations - Hardware acceleration with NVIDIA platforms |
| Data Analysis and Visualization | 10% | - Visualization techniques for model behavior and results - Analyzing multimodal datasets and outputs - Interpretation of generative AI outputs |
| Software Development and Engineering | 15% | - Best practices for building and maintaining systems - Libraries, frameworks, and tools for multimodal AI - Development workflows for generative AI applications |
NVIDIA Generative AI Multimodal Sample Questions:
Question 1
In the context of multimodal machine learning, what does 'data fusion' refer to?
A. Removing missing or incomplete information from different modalities.
B. Separating different modalities of data into distinct representations.
C. Combining different modalities of data into a single representation.
D. Evaluating the quality of diverse data types in multimodal machine learning.
Question 2
You want to evaluate the performance of an AI model. Which of the following is a method for AI model evaluation?
A. Interviewing the developers of the AI model to assess its performance.
B. Calculating the loss function of the model on the training set.
C. Calculating the model's accuracy from randomly selected data points from the dataset not used during the model's training.
D. Randomly selecting data points from the training set and calculating the accuracy of the model on these data points.
Question 3
Which of the following tasks can be performed using the transformer LLM encoder model?
A. Speech recognition
B. Semantic analysis
C. Image generation
D. Generating code
Question 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 different types of data, such as text, images, audio, time series, and geospatial information.
C. Training a model using multiple precision levels, such as using both single-precision and double- precision floating-point numbers.
D. Training a model using diverse data types while addressing challenges related to missing or incomplete information.
Question 5
What is the role of CLIP (Contrastive Language-Image Pretraining) in text-to-image generation?
A. CLIP is used to convert textual input into image embeddings.
B. CLIP is used to enhance datasets through data augmentation for text-to-image generation.
C. CLIP provides a common embedding space for both the textual and image modalities.
D. CLIP is used to generate image captions from textual input.
Solutions:
| Question 1 Answer: C | Question 2 Answer: C | Question 3 Answer: B | Question 4 Answer: C | Question 5 Answer: C |
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