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| Section | Objectives |
|---|---|
| Multimodal AI Systems | - Cross-modal learning
|
| NVIDIA AI Ecosystem | - NVIDIA tools and frameworks
|
| Responsible and Trustworthy AI | - Ethical AI principles - Bias and safety considerations |
| Generative AI Concepts | - Generative models
|
| Core AI and Machine Learning Fundamentals | - Machine learning basics
|
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NEW QUESTION # 10
You're training a multimodal model for generating stories from images and audio. You use a Transformer architecture. During training, you notice that the model struggles to maintain long-range dependencies in the generated stories, leading to incoherent narratives. Which of the following techniques would be MOST effective in addressing this issue within the Transformer architecture?
Answer: E
Explanation:
Positional encodings help the Transformer understand the order of words in the sequence, which is crucial for maintaining coherence. Increasing the attention window size allows the model to attend to a larger context when generating each word, enabling it to capture longer-range dependencies. Reducing layers or embedding dimension would likely worsen the problem. Removing self-attention would defeat the purpose of using a Transformer. Positional encodings and attention window size are key to transformer performance with respect to long range dependencies.
NEW QUESTION # 11
You are building a multimodal model that combines text and image data to generate captions. The text encoder is a pre-trained BERT model, and the image encoder is a ResNet-50. You observe that the generated captions are heavily biased towards descriptions based on the text input, and the image information is not well represented. Which of the following techniques could you apply to improve the contribution of the image modality?
Answer: D
Explanation:
Applying a modality-specific loss weight allows you to explicitly control the importance of each modality during training. By increasing the weight of the image loss, you encourage the model to pay more attention to the image information and generate captions that are more representative of the visual content. Increasing BERT's learning rate could worsen the imbalance. PCA is a data reduction technique not a balancing technique, freezing the weights on resnet 50, will not allow the network to learn about the relationship, batch size is to do with training speed not modality balance.
NEW QUESTION # 12
In experimentation, how does data augmentation contribute to improving model accuracy?
Answer: D
Explanation:
Data augmentation applies label-preserving transformations to existing training examples - rotation, flipping, cropping, color jitter, and noise injection for images; back-translation, synonym substitution, and random masking for text; time-stretching, pitch-shifting, and noise addition for audio - to synthetically expand the effective size and diversity of a training dataset without collecting new labeled data. This exposes the model to a wider range of input variations it may encounter at inference time, reducing overfitting to the specific characteristics of the original, smaller dataset and improving generalization to unseen data. This is particularly valuable in domains where labeled data is expensive or scarce to collect, including many multimodal settings.
Options B, C, and D each misattribute augmentation's mechanism or effect: augmentation does not reduce model complexity (B) - it operates entirely on the data, leaving model architecture and parameter count unchanged, and can in some cases make optimization more demanding due to increased input variability. It is not merely a visualization tool with no accuracy impact (C) - this directly contradicts augmentation's well- established, empirically demonstrated role as a regularization technique. And while augmented data can occasionally surface edge cases during error analysis, augmentation's primary purpose is not interpretability (D) - techniques like SHAP, LIME, or attention visualization address interpretability directly, a separate concern from dataset expansion.
Reference: Experimentation domain - data augmentation as a regularization and generalization technique.
NEW QUESTION # 13
In a multimodal machine learning context, how are different modalities usually linked to each other?
Answer: B
Explanation:
The defining goal of multimodal machine learning is to learn a shared (joint) representation space that captures cross-modal relationships and correspondences - allowing information from one modality to inform, constrain, or complete information from another. This shared representation is what enables tasks like cross-modal retrieval (finding images from a text query), cross-modal generation (text-to-image, image-to- text), and joint reasoning (visual question answering), all of which require the model to relate concepts across modality boundaries rather than process each in isolation.
How that shared representation is learned varies - contrastive objectives (CLIP), joint embedding via co- attention (VisualBERT, LXMERT), or fusion layers that combine modality-specific features - but the underlying principle is consistent across architectures: linkage happens through learned representations, not fixed rules or arbitrary connections.
Option C describes a specific, narrow ensembling strategy (tree-based combination of separate unimodal models) that is neither standard nor representative of how modern multimodal systems establish cross-modal relationships; it also conflates "linking modalities" with "combining model outputs," which is closer to late fusion than to representation learning. Option D is simply the negation of the field's core premise. Option B introduces randomness where structure is explicitly what is being learned.
Reference: Multimodal Data domain - shared/joint representation learning as the foundation of multimodal ML.
NEW QUESTION # 14
A research team has developed a novel multimodal model that fuses text, image, and audio dat a. They want to quantitatively evaluate the model's performance in comparison to several existing state-of-the-art models. Which of the following evaluation metrics would be MOST appropriate to assess the model's ability to generate coherent and relevant text descriptions based on the combined multimodal input?
Answer: A
Explanation:
BLEU and ROUGE are standard metrics for evaluating text generation tasks by comparing the generated text to reference texts. They assess the similarity and overlap in terms of n-grams. Perplexity measures the uncertainty of a language model. Inception Score and FID are used for evaluating image generation quality. SSIM measures the similarity between two images.
NEW QUESTION # 15
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