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NVIDIA NCA-GENM Exam Syllabus Topics:

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

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

NEW QUESTION # 24
Consider the following code snippet used within a U-Net architecture. What is its purpose?
torch.cat ([up, skip], dim=1)

A. It concatenates the 'up' and 'skip' tensors along the channel dimension.
B. It performs a matrix multiplication between the 'up' and 'skip' tensors.
C. It performs an element-wise addition of the 'up' and 'skip' tensors.
D. It subtracts the 'skip' tensor from the 'up' tensor.
E. It multiplies the 'up' and 'skip' tensors element-wise.

Answer: A

Explanation:
The 'torch.cat([up, skip], dim=1) function concatenates two tensors, 'up' and 'skip' , along the channel dimension (dim=1) In the context of a U-Net, 'up' represents the upsampled feature map from the decoder path, and 'skip' represents the corresponding feature map from the encoder path. Concatenating them allows the decoder to combine both coarse-grained and fine-grained information for better image reconstruction.

NEW QUESTION # 25
You are training a multimodal generative A1 model that takes text and images as input to generate videos. During experimentation, you observe that the model performs well on common scenarios (e.g., 'a dog playing in the park') but struggles to generate coherent videos for less frequent or abstract scenarios (e.g., 'the concept of time flowing'). What is the MOST effective strategy to improve the model's performance on these challenging scenarios, focusing on test data quality?

A. Curate a new test dataset specifically containing challenging scenarios and use it to evaluate and fine-tune the model. Ensure the new test data includes diverse interpretations and variations of the abstract concepts.
B. Reduce the complexity of the model architecture to prevent overfitting on the common scenarios.
C. Increase the size of the training dataset by duplicating existing common scenario examples.
D. Train the model for a significantly longer duration on the existing training data.
E. Implement data augmentation techniques on the existing training data, focusing on color adjustments and minor image transformations.

Answer: A

Explanation:
Curating a specific test dataset for challenging scenarios allows for targeted evaluation and fine-tuning. Duplicating common scenarios (A) won't address the problem. Simple augmentations (B) may not be sufficient. Reducing model complexity (D) might hurt overall performance. Longer training (E) without addressing data bias is unlikely to help significantly.

NEW QUESTION # 26
You're developing a system that analyzes video footage and generates textual summaries of the events occurring in the video. Which of the following architectures would be the MOST appropriate starting point for this task?

A. A transformer-based encoder-decoder architecture, with the encoder processing video frames (e.g., using a 3D CNN) and the decoder generating text.
B. A purely convolutional neural network (CNN) for image classification.
C. A generative adversarial network (GAN) for image generation.
D. A support vector machine (SVM) for classification.
E. A long short-term memory (LSTM) network for time series forecasting.

Answer: A

Explanation:
Transformer-based encoder-decoder architectures are well-suited for sequence-to-sequence tasks like video captioning. The encoder can process the video frames to extract features, and the decoder can generate the corresponding textual summary. A 3D CNN can be used to extract spatio-temporal features from the video.

NEW QUESTION # 27
In LLM evaluation, what does "zero-shot learning" refer to?

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

Answer: B

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 # 28
You are fine-tuning a large pre-trained language model for a specific downstream task. During training, you observe that the model performs well on the training data but generalizes poorly to the validation dat a. Which of the following strategies could help improve the model's generalization performance?

A. Implement early stopping based on the validation loss.
B. Increase the training data size by collecting more data.
C. Decrease the learning rate.
D. Increase the weight decay (L2 regularization).
E. Increase the learning rate.

Answer: A,B,C,D

Explanation:
Decreasing the learning rate can help the model to converge to a better solution and avoid overfitting. Increasing the training data size provides the model with more examples to learn from, improving generalization. Early stopping prevents the model from training for too long and overfitting the training data. Increasing weight decay adds more regularization, preventing the model from learning overly complex patterns. Increasing the learning rate might worsen overfitting.

NEW QUESTION # 29
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