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NVIDIA NCA-GENM Exam Syllabus Topics:

Section	Weight	Objectives
Performance Optimization	10%	- Techniques for optimizing AI performance - Monitoring and improving system efficiency
Software Development & Engineering	15%	- Python libraries for multimodal AI - Integration and deployment of multimodal AI systems
Data Analysis & Visualization	10%	- Visualization techniques for multimodal data - Data preprocessing and feature engineering
Experimentation	25%	- A/B testing - Experimental design - Model evaluation and comparison - Hypothesis testing
Core ML & AI Knowledge	20%	- Key algorithms and techniques - Basic concepts and terminology
Trustworthy AI	5%	- Ethical considerations in AI development - Ensuring fairness and transparency
Multimodal Data	15%	- Applications and use cases - Handling and integrating text, image, and audio data

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NVIDIA NCA-GENM Exam Questions Are Out - Download And Prepare [2026]

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NVIDIA Generative AI Multimodal Sample Questions (Q20-Q25):

NEW QUESTION # 20
Which of the following statements accurately describes the role of attention mechanisms in Transformer-based multimodal models?
(Select all that apply)

A. Attention mechanisms allow the model to focus on the most relevant parts of the input sequence when generating the output.
B. Attention mechanisms are used to compress the input sequence into a fixed-length vector representation.
C. Attention mechanisms enable the model to learn relationships between different modalities, such as images and text.
D. Attention mechanisms prevent vanishing gradients during training of deep neural networks.
E. Attention mechanisms are primarily used to reduce the computational cost of processing long sequences.

Answer: A,C

Explanation:
Attention mechanisms enable the model to selectively focus on relevant parts of the input and learn relationships between modalities. They don't compress the input into a fixed-length vector, nor are they primarily for reducing computational cost or preventing vanishing gradients (although they can indirectly help with the latter).

NEW QUESTION # 21
You are using NeMo to fine-tune a pre-trained language model for a specific text generation task. You want to implement a custom data augmentation technique to improve the model's robustness. Which of the following approaches is most appropriate for integrating your custom augmentation within the NeMo framework?

A. Monkey-patch the existing NeMo data loading functions to inject your augmentation logic.
B. Create a custom *Dataset* class that inherits from 'nemo.core.Dataset' and implements your augmentation within the '_getitem
C. Augment the data directly within the training loop, applying transformations to each batch before feeding it to the model. method.
D. Use a separate data processing pipeline outside of NeMo and save the augmented data to disk before training.
E. Modify the core NeMo library files to directly incorporate your augmentation logic.

Answer: B

Explanation:
Creating a custom 'Dataset' class that inherits from 'nemo.core.Dataset' is the recommended and most maintainable way to integrate custom data augmentation in NeMo. This allows you to leverage NeMo's data loading and processing pipelines while seamlessly incorporating your specific augmentation logic within the '_getitem method. Modifying core NeMo files (A) is strongly discouraged. Using a separate pipeline (C) disconnects augmentation from the NeMo workflow. Monkey-patching (D) is brittle. Augmenting within the training loop (E) can be inefficient.

NEW QUESTION # 22
Consider the following code snippet used in training a multimodal model:

During experimentation, you discover that the image modality contributes negligibly to the final prediction. How would you modify the training loop to dynamically adjust the importance of each modality?

A. Implement a separate loss function for the image modality and adjust its weight based on validation performance.
B. Apply a fixed weight to the image features before feeding them into the model.
C. Introduce a modality dropout mechanism that randomly drops either the image or text modality during each training iteration.
D. Use a curriculum learning approach where the model is initially trained only on the text modality, and the image modality is gradually introduced.
E. Compute modality-specific gradients and apply a scaling factor to the image gradients based on their magnitude relative to the text gradients.

Answer: E

Explanation:
Dynamically scaling gradients based on their magnitude allows the model to automatically adjust the importance of each modality during training. If the image gradients are small compared to the text gradients, the scaling factor will increase their influence, encouraging the model to learn from the image modality. Modality dropout is helpful, however gradient scaling provides finer control.

NEW QUESTION # 23
You are training a Variational Autoencoder (VAE) and notice that the generated samples are blurry and lack detail. Which of the following adjustments could help improve the quality and sharpness of the generated images2 Select all that apply.

A. Decrease the weight of the Kullback-Leibler (KL) divergence term in the loss function-
B. Increase the dimensionality of the latent space
C. Increase the capacity of the encoder and decoder networks by adding more layers or units.
D. Use a more powerful decoder architecture, such as one with deconvolutional layers.
E. Decrease the batch size to reduce computational complexity

Answer: A,B,C,D

Explanation:
Increasing network capacity allows the model to learn more complex representations. Decreasing the KL divergence allows the decoder to focus more on reconstruction, potentially sacrificing some disentanglement. Increasing the latent space provides more room for capturing variations in the data. Using a more powerful decoder helps in generating sharper images

NEW QUESTION # 24
You're evaluating the performance of a video captioning model. The model generates captions for video clips. You notice that while the captions are generally accurate, they often lack detail and creativity. Which metric(s) would be MOST suitable for assessing the diversity and originality of the generated captions? (Select all that apply)

A. ROUGE (Recall-Oriented Understudy for Gisting Evaluation)
B. SPICE (Semantic Propositional Image Caption Evaluation)
C. Self-BLEU
D. CIDEr (Consensus-based Image Description Evaluation)
E. BLEU (Bilingual Evaluation Understudy)

Answer: C

Explanation:
Self-BLEU calculates the BLEU score of a set of generated captions against themselves. A lower Self-BLEU score indicates higher diversity, as it means the captions are less similar to each other. The other metrics (BLEU, CIDEr, SPICE, ROUGE) primarily focus on accuracy and semantic similarity to reference captions, not diversity. Although, SPICE could capture the semantic similarity of different captions, Self-BLEU is most used.

NEW QUESTION # 25
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