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NVIDIA NCA-GENM Exam Syllabus Topics:

Section	Objectives
Topic 1: Generative AI Concepts	- Generative models 1. Transformers and LLM basics 2. Diffusion models
Topic 2: Responsible and Trustworthy AI	- Ethical AI principles - Bias and safety considerations
Topic 3: NVIDIA AI Ecosystem	- NVIDIA tools and frameworks 1. NeMo framework usage 2. GPU-accelerated AI workflows
Topic 4: Core AI and Machine Learning Fundamentals	- Machine learning basics 1. Neural networks fundamentals 2. Supervised and unsupervised learning
Topic 5: Multimodal AI Systems	- Cross-modal learning 1. Audio-visual understanding 2. Text-image integration - Multimodal model design

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NVIDIA Generative AI Multimodal Sample Questions (Q17-Q22):

NEW QUESTION # 17
Consider a scenario where you are developing a multimodal system for generating 3D models from text descriptions. The system uses a Variational Autoencoder (VAE) to generate the 3D models. During training, you observe that the generated 3D models lack diversity and tend to cluster around a few common shapes. Which of the following techniques could you employ to improve the diversity of the generated 3D models?

A. Decreasing the capacity of the VAE's latent space.
B. Applying techniques like adversarial training to encourage the VAE to generate more realistic 3D models.
C. Using a larger training dataset with more diverse text descriptions.
D. Decreasing the batch size during training.
E. Increasing the weight of the Kullback-Leibler (KL) divergence term in the VAE's loss function.

Answer: B,C

Explanation:
A larger, more diverse dataset provides more examples for the VAE to learn from, leading to more diverse generated outputs. Adversarial training can help the VAE generate more realistic and diverse 3D models by penalizing outputs that are easily distinguishable from real data. Decreasing the latent space capacity can limit the model's ability to capture the diversity of the data. Increasing the KL divergence weight can lead to underfitting and less diverse outputs. Decreasing batch size can increase the variance of gradients during training, but its impact on diversity is less direct.

NEW QUESTION # 18
What role does 'late fusion' play in multimodal machine learning?

A. It refers to the process of combining multiple modalities at the decision level.
B. It refers to the process of combining multiple modalities at the training stage.
C. It refers to the process of combining multiple modalities at the feature level.
D. It refers to the process of combining multiple modalities at the preprocessing stage.

Answer: A

Explanation:
Late fusion trains separate, independent models for each modality - each producing its own prediction, score, or decision - and combines those outputs only at the final decision stage, typically via averaging, weighted voting, a learned meta-classifier (stacking), or simple rule-based aggregation. This is the direct counterpart to early fusion (combination at the raw/feature input level, tested elsewhere in this set) and to intermediate/hybrid fusion (combination at one or more mid-network representation levels).
Late fusion's key practical advantage is modularity and robustness: because each modality's model operates independently until the final combination step, a missing or corrupted modality at inference time degrades performance gracefully rather than catastrophically - the surviving modalities' models can still contribute a prediction. It also allows each modality-specific model to be trained, validated, and even updated independently, which simplifies engineering in production systems. Its main disadvantage is that it cannot capture fine-grained, low-level cross-modal interactions, since by the time information reaches the fusion point, each modality has already been reduced to a high-level decision.
Options C and D describe feature-level and preprocessing-level combination respectively - both inconsistent with "late" in the fusion terminology, which specifically denotes the decision/output stage. Option B describes a training-schedule concept unrelated to fusion architecture.
Reference: Multimodal Data domain - late fusion vs. early/hybrid fusion, decision-level combination.

NEW QUESTION # 19
You've trained a large multimodal model that takes text and images as input and generates creative stories. While the model produces high-quality stories in general, it occasionally generates outputs that are factually incorrect or nonsensical. Which of the following techniques would be MOST effective in improving the model's factual accuracy and coherence?

A. Increasing the model size by adding more layers.
B. Reducing the temperature parameter during generation.
C. Removing dropout layers.
D. Training the model on a smaller dataset.
E. Implementing a retrieval-augmented generation (RAG) approach.

Answer: E

Explanation:
Retrieval-augmented generation (RAG) involves retrieving relevant information from an external knowledge source (e.g., a database or a collection of documents) and incorporating it into the model's generation process. This can help the model generate more factually accurate and coherent outputs by grounding its responses in external knowledge. Increasing the model size might improve performance, but doesn't guarantee factual accuracy. Reducing temperature makes the output more predictable but doesn't address factual correctness. Smaller datasets and removing dropout would likely worsen performance.

NEW QUESTION # 20
You are building a multimodal emotion recognition system that takes both facial expressions (images) and speech audio as input. During development, you observe that the model is heavily biased towards the audio modality, effectively ignoring the visual input. Which technique would be the LEAST effective in mitigating this modality bias?

A. Increasing the complexity of the audio processing branch and simplifying the image processing branch of the model.
B. Reweighting the loss function to penalize errors made based on the less dominant modality (image).
C. Adversarial training to make each modality indistinguishable.
D. Gradient blending: Adjusting the gradients from each modality based on their relative importance.
E. Modality dropout: Randomly dropping out one of the modalities during training.

Answer: A

Explanation:
Increasing the audio branch's complexity while simplifying the image branch would actually exacerbate the modality bias towards audio. The other techniques (modality dropout, gradient blending, loss reweighting, and adversarial training) are all strategies designed to encourage the model to utilize both modalities more evenly. Increasing the model parameters of one mode leads to over-representation of that mode.

NEW QUESTION # 21
You are developing a multimodal generative model that takes a text description as input and generates a corresponding image. However, you notice that the generated images often lack fine-grained details and realism. Which of the following approaches could you employ to improve the quality and realism of the generated images? (Select all that apply)

A. Decrease the size of the text encoder.
B. Use a higher-resolution image generator architecture.
C. Train the model using a generative adversarial network (GAN) framework.
D. Implement a loss function that encourages the generated images to match the statistical distribution of real images.
E. Use a smaller training dataset.

Answer: B,C,D

Explanation:
Using a higher-resolution generator architecture allows the model to generate more detailed images. GANs are known for their ability to generate realistic images. A loss function that encourages the generated images to match the distribution of real images can also improve realism. Decreasing text encoder size or using a smaller dataset will hurt performance.

NEW QUESTION # 22
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