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NVIDIA NCA-GENM Exam Syllabus Topics:

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

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NVIDIA Generative AI Multimodal Sample Questions (Q48-Q53):

NEW QUESTION # 48
You are using NeMo to fine-tune a large language model for a specific task. You notice that the model is overfitting to the training dat a. Which of the following techniques could you apply to mitigate overfitting in this scenario? (Select all that apply)

A. Increase the batch size.
B. Increase the size of the training dataset.
C. Decrease the learning rate.
D. Implement weight decay (L2 regularization).
E. Add dropout layers to the model architecture.

Answer: B,C,D,E

Explanation:
Overfitting occurs when a model learns the training data too well and performs poorly on unseen data. Increasing the size of the training dataset provides the model with more diverse examples. Decreasing the learning rate helps the model converge to a more generalizable solution. Weight decay penalizes large weights, preventing the model from becoming too specialized to the training data. Dropout randomly disables neurons during training, forcing the model to learn more robust features. Increasing batch size can sometime prevent model to converge You are developing a system that uses a generative AI model to create personalized avatars for users based on their descriptions.

NEW QUESTION # 49
For building a zero-shot image classification pipeline, what could be a crucial step in the process?

A. Using a model like CLIP for encoding both images and their textual descriptions into a shared representation space for comparison.
B. Focusing on enhancing the resolution and quality of images before classification.
C. Manually labeling each image in the dataset for precise classification.
D. Designing an algorithm to replace the need for textual descriptions in the classification process.

Answer: A

Explanation:
Zero-shot image classification, by definition, requires classifying images into categories the model was never explicitly trained to recognize, with no task-specific labeled examples. CLIP-style models enable this by encoding both images and candidate text labels (e.g., "a photo of a {class}") into a shared embedding space; classification then reduces to a similarity comparison - computing cosine similarity between the image embedding and each candidate text embedding and selecting the closest match. This is the crucial architectural step: without a shared embedding space linking visual and textual semantics, there is no mechanism to generalize to unseen classes using only their names or descriptions.
Option B directly contradicts the "zero-shot" premise - manual labeling of the target dataset is precisely what zero-shot classification is designed to avoid; if labels were being collected for the target classes, the task would be standard supervised classification, not zero-shot. Option A (image enhancement) may marginally help downstream accuracy but is not the crucial, defining step. Option D is incoherent with how CLIP-style zero-shot classification actually works - the textual description of each candidate class is the essential input that makes zero-shot generalization possible; eliminating it would remove the mechanism entirely, not improve it.
Reference: Multimodal Data domain - zero-shot classification via shared embedding spaces (CLIP).

NEW QUESTION # 50
Consider the following code snippet, where you are trying to load image and text data for a multimodal model. What is the most likely cause of error if the code fails during the image loading step?

A. The image files are corrupted or in an unsupported format.
B. The batch size is too large.
C. The learning rate is set too high.
D. The text data is not in IJTF-8 encoding.
E. The system doesn't have CUDA drivers installed.

Answer: A

Explanation:
Since the error occurs specifically during the image loading step, the most likely cause is related to the image files themselves. Corrupted files or unsupported formats would prevent the image loading library from successfully reading the images. The other options are less likely to cause an error specifically during image loading.

NEW QUESTION # 51
Consider you are working on a project that aims at generating photorealistic images from segmentation maps, using a conditional GAN architecture. The training process is unstable, frequently exhibiting mode collapse and artifacts. Describe a series of techniques, ranked by their likely impact, to mitigate these issues.

A. 1. Increase batch size. 2. Decrease learning rate. 3. Add more convolutional layers.
B. 1. Reduce the number of layers in the discriminator. 2. Increase the learning rate of the generator. 3. Disable batch normalization.
C. 1. Switch to a Transformer-based architecture. 2. Use a larger dataset. 3. Decrease the number of channels in the generator.
D. 1. Implement Spectral Normalization. 2. Use PatchGAN discriminator. 3. Apply data augmentation (e.g., random flips, jitter).
E. 1. None of the above

Answer: D

Explanation:
Spectral Normalization stabilizes training by limiting the Lipschitz constant of the discriminator. PatchGAN discriminator focuses on local image patches, improving detail and reducing artifacts. Data augmentation increases the diversity of training data and improves generalization. Thus, Option B presents the most impactful techniques, ranked appropriately. Other options either suggest less impactful techniques or recommend steps that could worsen the issues. Mode Collapse can be avoided here with data augmentation.

NEW QUESTION # 52
A financial institution is developing a multimodal A1 system to detect fraudulent transactions by analyzing transaction details (text), user images, and audio recordings of phone calls. Which of the following strategies is MOST crucial for handling the missing data that frequently occurs across these modalities?

A. Using a modality dropout technique during training, randomly masking modalities to force the model to learn robust representations from incomplete data.
B. Replacing missing data with a single, arbitrary placeholder value (e.g., -1 for numerical data, 'missing' for text) across all modalities.
C. Ignoring transactions with missing data to simplify the model's training process.
D. Employing a joint imputation approach that leverages information from available modalities to predict and fill in missing values in other modalities.
E. Imputing missing data in each modality independently using modality-specific imputation techniques (e.g., mean imputation for numerical data, most frequent category for categorical data).

Answer: A,D

Explanation:
Ignoring missing data or using simple imputation techniques can introduce bias and reduce the model's accuracy. A joint imputation approach is superior because it leverages the relationships between modalities to improve imputation accuracy. Modality dropout during training further enhances robustness to missing data in real-world scenarios.

NEW QUESTION # 53
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