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| Section | Objectives |
|---|---|
| Generative AI Concepts | - Generative models
|
| Multimodal AI Systems | - Cross-modal learning
|
| NVIDIA AI Ecosystem | - NVIDIA tools and frameworks
|
| Responsible and Trustworthy AI | - Ethical AI principles - Bias and safety considerations |
| Core AI and Machine Learning Fundamentals | - Machine learning basics
|
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NEW QUESTION # 52
Consider the following Python code snippet using PyTorch, intended for fusing image and text features in a multimodal model. Assume 'image_featureS and 'text_features' are tensors of shape Which of the following fusion methods is implemented in this code?
Answer: C
Explanation:
The code snippet uses 'torch.cat((image_features, text_features), dim=l y , which concatenates the image and text features along the feature dimension (dim=l This is a concatenation-based fusion method. Addition would involve 'image_features + text_featureS. Gated or cross-modal attention would involve learnable weights and attention mechanisms. Tensor product involves reshaping and matrix multiplication of the features. The given answer is A, but the code snippet is missing. Please provide the code for an accurate explanation.
NEW QUESTION # 53
You are developing a GenAI-Multimodal system that uses data from various sources. What is one potential issue you need to consider in relation to bias in data?
Answer: C
Explanation:
Representativeness bias occurs when a training dataset systematically over- or under-samples subpopulations relative to the population the deployed system will actually encounter - for example, a facial recognition dataset skewed toward lighter-skinned faces, or a multimodal medical dataset drawn predominantly from one demographic group. Because models learn statistical patterns from their training distribution, an unrepresentative dataset produces a model whose accuracy, calibration, and fairness properties degrade for underrepresented groups, even when aggregate accuracy metrics look acceptable.
This is precisely why aggregate accuracy is an insufficient safeguard: option B's framing - that bias doesn't matter "as long as predictions are accurate" - conflates overall accuracy with subgroup accuracy, and a model can post strong aggregate numbers while systematically failing specific populations. Option D is factually false; AI systems have no inherent neutrality - they inherit and can amplify whatever patterns (including societal biases) exist in their training data and objective function. Option C is also incorrect:
mitigating representativeness bias is significantly cheaper and more effective when addressed at the data- collection and curation stage - through stratified sampling, bias audits, and diverse data sourcing - than after deployment, when it becomes a retraining and remediation problem, and by then real-world harm may have already occurred.
Reference: Trustworthy AI domain - data representativeness, fairness, bias identification and mitigation.
NEW QUESTION # 54
You are developing a ML model for image classification. You have a dataset with 10,000 images of cats, dogs and birds. Which of the following ML models would be the most appropriate choice for this task?
Answer: D
Explanation:
CNNs are the standard architecture for image classification because their convolutional layers exploit the spatial locality and translation invariance inherent to image data: learned filters detect local patterns (edges, textures, shapes) that compose hierarchically into higher-level features (parts, objects) as depth increases, without requiring the manual feature engineering that traditional models would need to reach comparable accuracy on raw pixel data. Pooling layers further provide a degree of spatial invariance, and parameter sharing across the image keeps the model tractable relative to a fully connected network operating on raw pixels.
Logistic Regression (A) is a linear classifier that operates on flattened feature vectors; applied directly to raw pixels of a 3-class image problem, it cannot capture the non-linear spatial structure needed to separate cats, dogs, and birds reliably, though it could serve as a baseline or as the final classification head atop CNN- extracted features. K-Means (B) is an unsupervised clustering algorithm - inappropriate here because the task is supervised classification with labeled classes. Linear Regression (C) predicts continuous outputs and is not designed for categorical class prediction at all.
For 10,000 labeled images, a CNN (potentially fine-tuned from a pretrained backbone via transfer learning, given the modest dataset size) is the appropriate and industry-standard choice.
Reference: Core Machine Learning and AI Knowledge domain - CNN architecture for image classification.
NEW QUESTION # 55
You're developing a multimodal model that takes both image and audio inputs to predict a relevant text description. You observe that the model is heavily biased towards the image data, effectively ignoring the audio input. Which of the following techniques could you employ to address this modality imbalance and ensure the model effectively utilizes both input modalities?
Answer: B,C,D,E
Explanation:
Increasing the learning rate for the audio pathway allows it to update its weights more aggressively, potentially counteracting the image bias. Applying modality-specific dropout to the image pathway forces the model to rely less on image features and more on audio. Oversampling the audio data ensures that the model sees more examples from the audio modality during training. Reducing the dimensionality of the image features can prevent them from dominating the fusion process. Increasing batch size is not specific to each modality and does not directly deal with modality imbalance, but can influence training dynamics.
NEW QUESTION # 56
You are building a multimodal Generative AI system to generate marketing content. You have text descriptions of products, images of the products, and customer reviews. Which of the following strategies would best handle potential inconsistencies or contradictions between these different modalities?
Answer: D
Explanation:
Employing an attention mechanism or a cross-modal fusion network allows the model to dynamically learn which modalities are most relevant for a given input or generation task. This helps in resolving inconsistencies by giving more weight to reliable modalities and diminishing the influence of less reliable ones. Simple averaging or prioritization can lead to suboptimal results when modalities contradict each other.
NEW QUESTION # 57
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