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NVIDIA NCA-GENM Exam Syllabus Topics:

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

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NVIDIA Generative AI Multimodal Sample Questions (Q38-Q43):

NEW QUESTION # 38
You're building a multimodal model that processes both images and text. The image encoder outputs a feature vector of size 2048, and the text encoder outputs a feature vector of size 512. Which of the following strategies is MOST appropriate for combining these feature vectors before feeding them into a downstream classifier?

A. Project both feature vectors into a common embedding space of size 256 using separate linear layers, then concatenate the resulting vectors.
B. Concatenate the two feature vectors directly, resulting in a combined vector of size 2560.
C. Average the two feature vectors, resulting in a combined vector of size 2048
D. Project both feature vectors into a common embedding space of size 2048 using separate linear layers, then average the resulting vectors.
E. Project both feature vectors into a common embedding space of size 512 using separate linear layers, then concatenate the resulting vectors.

Answer: A

Explanation:
Projecting into a lower-dimensional common embedding space reduces dimensionality and potential overfitting, while concatenation after projection retains information from both modalities. Averaging might mask distinct features. Projecting to original vector sizes before averaging is possible, but concatenating smaller projected vectors is most memory efficient

NEW QUESTION # 39
In LLM evaluation, what does "zero-shot learning" refer to?

A. A technique to reduce training time to zero
B. The model's performance after extensive training
C. The model's ability to perform tasks it has not been explicitly trained on
D. The model's ability to learn from zero examples

Answer: C

Explanation:
Zero-shot learning describes a model's capacity to correctly perform a task it was never explicitly trained or fine-tuned on, relying instead on knowledge and generalization ability acquired during broader pretraining.
For LLMs, this typically means the model is given only a natural-language instruction or prompt describing the task - with no task-specific labeled examples provided in the prompt at all - and is expected to produce a reasonable response by generalizing from its pretraining. This is directly analogous to CLIP's zero-shot image classification (covered elsewhere in this set): a model trained broadly can be applied to a new, specific task purely through how the task is described to it, without additional task-specific training.
Option A is a subtly incorrect paraphrase: zero-shot learning is not about the model "learning from zero examples" during a training process - it's about applying a model that was never trained for the specific task at all, at inference time. The model isn't learning in the zero-shot moment; it's generalizing from prior training. Option B misapplies "zero" to training time rather than to task-specific examples - an unrelated concept. Option C directly contradicts the definition; zero-shot specifically refers to performance *without* task-specific training, not performance *after* extensive training on that task.
Zero-shot is typically contrasted with few-shot learning, where a small number of task-specific examples are included in the prompt to guide the model's response without updating its weights.
Reference: Core Machine Learning and AI Knowledge domain - zero-shot vs. few-shot learning, generalization in LLMs.

NEW QUESTION # 40
Consider a scenario where you are building a multimodal model that combines image and text data for image captioning. You're using a transformer architecture with cross-attention. Which of the following best describes the role of cross-attention in this context?

A. It is primarily used for dimensionality reduction of the image features.
B. It allows the text embeddings to attend to the image features, enabling the model to generate captions based on relevant image regions.
C. It allows the image features to attend to themselves, highlighting the most salient regions in the image.
D. It enables the text embeddings to attend to themselves, capturing long-range dependencies within the text.
E. It fuses the image and text embeddings into a single representation before feeding them to the decoder.

Answer: B

Explanation:
Cross-attention in image captioning allows the decoder (generating text) to focus on specific parts of the image that are most relevant for generating the next word in the caption. The text 'attends' to the image.

NEW QUESTION # 41
You are developing a system to generate captions for videos. The video frames are processed using a pre-trained ResNet model, and the audio track is processed using a pre-trained Wav2Vec model. Which of the following techniques is MOST suitable for aligning the visual and audio features to generate accurate and coherent captions?

A. Using cross-attention mechanisms where the audio features attend to the visual features, and vice-versa, before feeding them into a Transformer decoder.
B. Training separate LSTMs for visual and audio features and averaging their outputs.
C. Using a simple feedforward network to combine the ResNet and Wav2Vec features.
D. Concatenating the ResNet and Wav2Vec features and feeding them into a single LSTM.
E. Ignoring the audio track and only using the video frames.

Answer: A

Explanation:
Cross-attention allows the model to learn the temporal relationships and dependencies between the visual and audio modalities. The audio features can attend to relevant visual features at each time step, and vice versa, leading to better alignment and more coherent captions. Simple concatenation and averaging are less effective at capturing these complex relationships. Ignoring the audio track loses valuable information.

NEW QUESTION # 42
Consider the following Python code snippet using PyTorch, intended to combine image and text embeddings:

Which of the following statements regarding the output shapes of these combined embeddings are TRUE? (Select TWO)

A. combined_embedding_weighted has shape (32, 1024).
B. combined_embedding_weighted has shape (32, 512).
C. combined_embedding_add has shape (32, 1024).
D. combined_embedding_concat has shape (32, 1024).
E. combined_embedding_concat has shape (64, 512).

Answer: B,D

Explanation:
torch.cat concatenates the embeddings along dimension 1, resulting in shape (32, 1024). Element-wise addition maintains the original shape (32, 512). The weighted sum is also element-wise, preserving the (32, 512) shape.

NEW QUESTION # 43
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