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NVIDIA NCA-GENM Exam Syllabus Topics:

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

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NVIDIA Generative AI Multimodal Sample Questions (Q24-Q29):

NEW QUESTION # 24
You're working on a project involving multimodal transfer learning for generating recipes from images of dishes and ingredient lists. You have a large dataset of images but a limited dataset of paired images and ingredient lists. You decide to leverage a pre-trained image model and a pre-trained text model. However, you are facing catastrophic forgetting after fine-tuning the models on the paired image and ingredient list dat a. Which of the following techniques would be MOST effective in mitigating catastrophic forgetting while adapting the pre-trained models to the new task?

A. Freeze the weights of the pre-trained models and only train a small adapter module that bridges the gap between the pre-trained features and the recipe generation task.
B. Increase the batch size during fine-tuning.
C. Apply L1 regularization to the model weights.
D. Train the entire model from scratch on the limited paired dataset.
E. Use a very high learning rate during fine-tuning.

Answer: A

Explanation:
Using adapter modules is a common technique to mitigate catastrophic forgetting. By freezing most of the pre-trained weights and only training a small adapter, you preserve the knowledge learned during pre-training while adapting the model to the new task. Training from scratch would negate the benefits of transfer learning. A high learning rate can exacerbate forgetting. L1 regularization can prevent overfitting but doesn't directly address forgetting. Increasing batch size might improve generalization but doesn't solve the core issue of catastrophic forgetting.

NEW QUESTION # 25
You're building a system to translate speech to text using an encoder-decoder architecture with attention. You observe that the translated text often repeats phrases from the input speech. Which regularization techniques could help mitigate this issue? (Select TWO)

A. Decreasing the number of attention heads.
B. Increasing the size of the vocabulary.
C. Applying label smoothing to the target sequences.
D. Adding dropout to the encoder and decoder layers.
E. Adding L1 regularization to the embedding layer.

Answer: C,D

Explanation:
Dropout prevents overfitting by randomly dropping neurons during training, forcing the network to learn more robust representations and reducing reliance on specific features. Label smoothing encourages the model to be less confident in its predictions, making it less likely to overfit to the training data and repeat phrases. The size of the vocabulary, the number of attention heads, and L1 regularization on embeddings are less directly related to preventing repetition.

NEW QUESTION # 26
What advantage does multimodal learning have over unimodal learning?

A. It requires fewer data samples for learning.
B. It can capture more complex patterns and relationships in data.
C. It is more reliable than unimodal learning.
D. It is easier to collect multimodal data than unimodal data.

Answer: B

Explanation:
Multimodal learning's principal advantage is access to complementary and, at times, redundant information across modalities that a single modality alone cannot provide - enabling the model to capture richer, more nuanced patterns and relationships. A sentiment analysis system that sees only text misses tone-of-voice cues available in audio and facial expression cues available in video; combining all three lets the model resolve ambiguity that any single modality would leave unresolved (sarcasm detected via mismatched text sentiment and vocal tone, for instance). This complementarity is the substantive, well-evidenced advantage of multimodal approaches in the research literature.
The other options overstate or misstate multimodal learning's properties: it does not inherently require fewer data samples (A) - in fact, multimodal models often require more data to learn reliable cross-modal correspondences, and can be more data-hungry in practice, particularly during pretraining. Reliability (C) is not an inherent, guaranteed property; multimodal systems introduce new failure modes, such as sensitivity to missing or corrupted modalities and to modality imbalance, that must be explicitly engineered against - reliability is not automatic. Multimodal data is also not inherently easier to collect (D); acquiring synchronized, aligned data across multiple modalities (e.g., paired audio-video-text with accurate timestamps) is typically harder and more resource-intensive than collecting a single modality.
Reference: Multimodal Data domain - complementarity of modalities, richer pattern capture.

NEW QUESTION # 27
You're training a large language model (LLM) and notice that it struggles to maintain consistency and context over long passages of text. Which of the following architectural modifications would be most effective in addressing this issue?

A. Using a smaller embedding dimension.
B. Reducing the number of layers in the transformer architecture.
C. Increasing the size of the vocabulary.
D. Implementing a sparse attention mechanism to reduce computational cost
E. Increasing the maximum sequence length the model can process.

Answer: E

Explanation:
Increasing the maximum sequence length allows the model to consider a larger context window when generating text, improving its ability to maintain consistency and coherence over longer passages. Other options might address other issues, but increasing sequence length directly tackles the problem of limited context.

NEW QUESTION # 28
You are fine-tuning a pre-trained large language model (LLM) for a specific text generation task using LoRA (Low-Rank Adaptation).
Which of the following statements accurately describes the benefits and limitations of using LoRA?

A. LoRA reduces the number of trainable parameters by inserting low-rank matrices into the original model layers, making fine-tuning more memory-efficient.
B. LoRA is not compatible with model parallelism techniques.
C. LoRA can improve the accuracy of the fine-tuned model compared to full fine-tuning by preventing overfitting.
D. LoRA allows for efficient task switching by only storing and loading the small LoRA parameters for different tasks, while keeping the original LLM weights frozen.
E. A and B.

Answer: E

Explanation:
LoRA significantly reduces the number of trainable parameters, enabling more memory-efficient fine-tuning, especially for large models. It also facilitates efficient task switching as only the small LoRA parameters need to be stored and loaded for different tasks. While LoRA can help prevent overfitting compared to full fine-tuning, it doesn't guarantee improved accuracy. LoRA can be effectively combined with model parallelism.

NEW QUESTION # 29
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