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Updated: Aug 25, 2026
No. of Questions: 58 Questions & Answers with Testing Engine
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NVIDIA NCA-GENM Exam Overview:
| Certification Vendor: | NVIDIA |
| Exam Name: | NVIDIA Certified Associate - Generative AI Multimodal |
| Exam Number: | NCA-GENM |
| Real Exam Qty: | 50-60 |
| Available Languages: | English |
| Exam Duration: | 60 minutes |
| Exam Format: | Multiple-choice |
| Passing Score: | Not publicly disclosed |
| Certificate Validity Period: | 2 years |
| Related Certifications: | NVIDIA Certified Associate - Generative AI LLMs (NCA-GENL) NVIDIA Certified Associate - AI Infrastructure and Operations (NCA-AIIO) |
| Exam Price: | $125 USD |
| 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 |
|---|---|---|
| Topic 1: Multimodal Data | 15% | - Handling and integrating text, image, and audio data - Applications and use cases |
| Topic 2: Software Development & Engineering | 15% | - Python libraries for multimodal AI - Integration and deployment of multimodal AI systems |
| Topic 3: Experimentation | 25% | - Hypothesis testing - A/B testing - Experimental design - Model evaluation and comparison |
| Topic 4: Performance Optimization | 10% | - Techniques for optimizing AI performance - Monitoring and improving system efficiency |
| Topic 5: Data Analysis & Visualization | 10% | - Data preprocessing and feature engineering - Visualization techniques for multimodal data |
| Topic 6: Core ML & AI Knowledge | 20% | - Basic concepts and terminology - Key algorithms and techniques |
| Topic 7: Trustworthy AI | 5% | - Ethical considerations in AI development - Ensuring fairness and transparency |
NVIDIA Generative AI Multimodal Sample Questions:
1. You are tasked with developing an image processing model using machine learning. You need to classify thousands of labeled images of cats and dogs. Which algorithm is commonly used for image classification?
A) Decision Trees
B) Convolutional Neural Networks (CNN)
C) Linear Regression
D) K-Means Clustering
2. Which of the following best describes the role of machine learning in handling multimodal data?
A) To enable models to learn from and interpret diverse data types.
B) To reduce the amount of data needed for accurate predictions.
C) To eliminate the need for human intervention in data analysis.
D) To focus on textual data analysis.
3. 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 generate image captions from textual input.
C) CLIP is used to enhance datasets through data augmentation for text-to-image generation.
D) CLIP provides a common embedding space for both the textual and image modalities.
4. Which of the following tasks can be performed using the transformer LLM encoder model?
A) Semantic analysis
B) Speech recognition
C) Generating code
D) Image generation
5. You have a dataset containing information about sales performance for different regions in the last ten years.
Which type of data visualization would be most appropriate to compare the sales performance across regions on a year-by-year basis?
A) Line chart
B) Scatter plot
C) Bar chart
D) Pie chart
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: A | Question # 3 Answer: D | Question # 4 Answer: A | Question # 5 Answer: D |
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