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Updated: Aug 15, 2026

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NVIDIA NCA-GENM Exam Overview:

Certification Vendor:NVIDIA
Exam Name:NVIDIA Certified Associate - Generative AI Multimodal
Exam Number:NCA-GENM
Certificate Validity Period:2 years
Exam Duration:60 minutes
Available Languages:English
Exam Format:Multiple-choice
Exam Price:$125 USD
Related Certifications:NVIDIA Certified Associate - Generative AI LLMs (NCA-GENL)
NVIDIA Certified Associate - AI Infrastructure and Operations (NCA-AIIO)
Passing Score:Not publicly disclosed
Real Exam Qty:50-60
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:

SectionWeightObjectives
Experimentation25%- A/B testing
- Experimental design
- Model evaluation and comparison
- Hypothesis testing
Multimodal Data15%- Applications and use cases
- Handling and integrating text, image, and audio data
Performance Optimization10%- Monitoring and improving system efficiency
- Techniques for optimizing AI performance
Data Analysis & Visualization10%- Data preprocessing and feature engineering
- Visualization techniques for multimodal data
Core ML & AI Knowledge20%- Basic concepts and terminology
- Key algorithms and techniques
Software Development & Engineering15%- Integration and deployment of multimodal AI systems
- Python libraries for multimodal AI
Trustworthy AI5%- Ensuring fairness and transparency
- Ethical considerations in AI development

NVIDIA Generative AI Multimodal Sample Questions:

1. What does 'modality alignment' refer to?

A) The process of integrating diverse data types such as text, images, audio, time series, and geospatial information.
B) Addressing challenges related to missing or incomplete information across different modalities.
C) The integration of pretrained models to perform custom tasks involving different types of data.
D) Aligning different modalities within multimodal data to ensure meaningful connections and associations.


2. What does 'kernel fusion' refer to in the context of AI model optimization?

A) Optimizing model inference by reducing the number of computations by pruning.
B) Using kernel functions to optimize model hyperparameters.
C) Combining multiple kernels into a single kernel for faster computation.
D) Applying multiple layers of kernels to improve model accuracy.


3. Assume you need to implement a multimodal pipeline to diagnose brain cancer type using MRI scans and their corresponding radiology reports. What do you need to include in the ablation study?

A) Directly combining MRI scans and radiology reports into a single input stream without preprocessing or modality-specific adjustments.
B) Training a deep learning model using the images in the dataset to find outliers and enhancing the quality of MRI scans using image processing techniques.
C) Implementing separate unimodal pipelines for each modality to ensure the data is informative and the model design is accurate.
D) More advanced natural language processing techniques to interpret radiology reports, ignoring the MRI scans' diagnostic value.


4. What are some methods to overcome limited throughput between CPU and GPU?

A) Using techniques like memory pooling.
B) Upgrade the GPU to a higher-end model.
C) Increase the clock speed of the CPU.
D) Increase the number of CPU cores.


5. In multimodal machine learning, what does 'early fusion' refer to?

A) Training separate models for each modality and then combining their predictions.
B) Integrating different modalities at the beginning of the model pipeline.
C) Implementing the model in the early stages of development of the ML solution.
D) Ignoring certain modalities and only using one modality for analysis and prediction.


Solutions:

Question # 1
Answer: D
Question # 2
Answer: C
Question # 3
Answer: C
Question # 4
Answer: A
Question # 5
Answer: B

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