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| Section | Objectives |
|---|---|
| Responsible and Trustworthy AI | - Bias and safety considerations - Ethical AI principles |
| Multimodal AI Systems | - Multimodal model design - Cross-modal learning
|
| NVIDIA AI Ecosystem | - NVIDIA tools and frameworks
|
| Generative AI Concepts | - Generative models
|
| Core AI and Machine Learning Fundamentals | - Machine learning basics
|
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NEW QUESTION # 16
Consider a multimodal generative model trained on a dataset of images and corresponding captions. After training, you observe that the model generates captions that are grammatically correct but often lack specific details and relevance to the input image. Which of the following regularization techniques is MOST likely to improve the faithfulness and informativeness of the generated captions?
Answer: B
Explanation:
Attention regularization is the most directly relevant technique. By penalizing the model when it fails to attend to relevant image regions, it encourages the model to focus on the important visual cues when generating the caption. This leads to more informative and faithful captions that are grounded in the image content. L1 regularization (A) promotes sparsity, Dropout (B) reduces overfitting, KL divergence regularization (C) encourages similarity to the prior distribution (which may not improve faithfulness), and adding Gaussian noise (E) improves robustness, but none of these directly address the issue of attention to relevant image regions.
NEW QUESTION # 17
What does 'modality alignment' refer to?
Answer: C
Explanation:
Modality alignment is the process of establishing correspondence between semantically related elements across different data types - for example, matching a spoken word to its corresponding lip movement in video, or a caption phrase to the image region it describes. It is distinct from fusion (combining modalities into a joint representation) and from data integration (option B, which describes ingestion rather than alignment). Alignment can be explicit, as in dynamic time warping for audio-text synchronization, or implicit, learned end-to-end through attention mechanisms such as cross-attention in transformer architectures. CLIP's contrastive objective is itself a form of learned alignment: it pulls matching image-text pairs together in embedding space while pushing non-matching pairs apart, producing an aligned shared representation without explicit temporal correspondence. Alignment quality directly affects downstream fusion: poorly aligned modalities introduce noise that fusion layers cannot fully compensate for, which is why alignment is typically treated as a prerequisite step, not an afterthought.
Option A describes model reuse for custom tasks (closer to transfer learning), while C describes handling missing modality data, a separate robustness concern. Neither captures the correspondence-building nature of alignment. On the NCA-GENM exam, expect alignment questions to be paired with fusion and co-embedding concepts.
Reference: Multimodal Data domain - modality alignment, co-embedding spaces, cross-modal attention.
NEW QUESTION # 18
You are training a Generative Adversarial Network (GAN) to generate realistic images. After several epochs, you observe that the generator is consistently producing similar images, regardless of the input noise. This phenomenon is known as:
Answer: B
Explanation:
Mode collapse occurs when the generator in a GAN learns to produce a limited variety of outputs, often focusing on the most easily generated examples. This leads to a lack of diversity in the generated images. Vanishing and exploding gradients are related to the training process itself, overfitting relates to model complexity compared to the training data, and underfitting relates to insufficient model capacity.
NEW QUESTION # 19
You are working with a large dataset of images for training a generative model. The dataset contains a significant amount of noise and outliers. Which of the following data preprocessing techniques would be MOST effective in mitigating the impact of noise and outliers on the model's performance?
Answer: E
Explanation:
Robust statistics-based normalization techniques, such as Z-score normalization using the median and interquartile range (IQR), are less sensitive to outliers than traditional methods like mean and standard deviation. Clipping pixel values can help to limit the impact of extreme outliers, but it may also remove valid data. Histogram equalization and Gaussian blur can improve image quality, but they are not specifically designed to handle outliers. Converting to grayscale reduces information but doesn't address noise specifically.
NEW QUESTION # 20
You are building a multimodal application that needs to understand both image and text dat a. You want to use a pre-trained model but fine-tune it for your specific task. Which of the following strategies is MOST effective for fine-tuning a large pre-trained multimodal model?
Answer: E
Explanation:
Fine-tuning the entire model with a small learning rate allows the model to adapt to the specific nuances of the new task while leveraging the knowledge already learned during pre-training. Freezing layers can limit adaptability. Training only a new head might not fully utilize the pre-trained features.
NEW QUESTION # 21
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