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| Section | Objectives |
|---|---|
| Core AI and Machine Learning Fundamentals | - Machine learning basics
|
| Responsible and Trustworthy AI | - Bias and safety considerations - Ethical AI principles |
| NVIDIA AI Ecosystem | - NVIDIA tools and frameworks
|
| Generative AI Concepts | - Generative models
|
| Multimodal AI Systems | - Multimodal model design - Cross-modal learning
|
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NEW QUESTION # 37
You are designing an experiment to compare two different multimodal A1 model architectures for video summarization. Model A is a transformer-based model, and Model B is a recurrent neural network (RNN)-based model. Which of the following evaluation metrics would be MOST appropriate for comparing the quality of the generated summaries, considering both content relevance and fluency?
Answer: C
Explanation:
ROUGE is a recall-based metric that effectively measures the overlap between the generated summary and reference summaries. It's well-suited for evaluating the content relevance of summaries. BLEU, while used for text generation, focuses on precision and might penalize summaries with different wording but similar meaning. Perplexity measures fluency but not relevance. MSE is inappropriate for text. Inception score is used primarily for images.
NEW QUESTION # 38
You have a multimodal model that processes images and text, and you want to deploy it on an edge device with limited computational resources. Which of the following hardware acceleration strategies would be MOST effective in improving the model's inference speed on the edge device?
Answer: B,D
Explanation:
NVIDIA TensorRT is specifically designed to optimize models for NVIDIA GPUs, which are commonly found in edge devices. Converting to a smaller architecture reduces the computational burden on the edge device. Distributed inference is complex to setup and cloud offloading defeats the purpose of edge deployments. A larger batch size requires more memory, which can be limiting on edge devices. Accepting a lower accuracy can improve inference, which can be acceptable for certain edge deployments
NEW QUESTION # 39
What is the purpose of a kernel in a Convolutional Neural Network (CNN)?
Answer: D
Explanation:
A kernel (or filter) in a CNN is a small matrix of learnable weights that slides across the input (an image, feature map, or intermediate activation) computing a dot product at each spatial position - the convolution operation. Each kernel is trained to detect a specific local pattern: early-layer kernels typically learn to detect low-level features like edges and color gradients, while kernels in deeper layers combine these into detectors for more complex, higher-level patterns (textures, object parts, and eventually whole-object representations as receptive fields grow with depth). A convolutional layer typically applies many kernels in parallel, each producing its own output channel, collectively forming the layer's feature map.
The other options describe separate CNN components with distinct responsibilities: the loss function (B) is computed at the network's output based on the difference between predictions and ground truth, entirely separate from the kernel's role in feature extraction. Classification (C) is typically performed by fully connected (dense) layers - often with a softmax activation - placed after the convolutional feature- extraction stack, not by the kernels themselves. Normalization (D) is handled by dedicated layers such as batch normalization or layer normalization, inserted between convolutional layers to stabilize activations, again a separate mechanism from the convolution operation itself.
Reference: Core Machine Learning and AI Knowledge domain - CNN architecture, kernels/filters, feature extraction.
NEW QUESTION # 40
Assume you need to implement a multimodal pipeline to diagnose brain cancer type using MRI scans and their corresponding radiology reports. What do you need to include in the ablation study?
Answer: C
Explanation:
An ablation study systematically removes or isolates individual components of a system to measure each one's individual contribution to overall performance. In a multimodal pipeline combining MRI scans and radiology reports, a proper ablation study requires training and evaluating separate unimodal pipelines - an image-only model on MRI scans alone, and a text-only model on radiology reports alone - alongside the full multimodal pipeline. Comparing these unimodal baselines against the combined system's performance is what actually demonstrates whether fusion is adding genuine diagnostic value beyond what either modality provides independently, and it surfaces whether one modality is doing most of the work while the other contributes marginally (or is even introducing noise) - critical information for both model design decisions and clinical validation in a high-stakes diagnostic context.
Option A describes an early-fusion design choice, not an ablation methodology - it's a modeling decision, not a validation technique for understanding component contribution. Option C proposes abandoning one modality's diagnostic value entirely, which undermines rather than tests the multimodal hypothesis. Option D describes data quality/preprocessing work relevant earlier in the pipeline, not the comparative, component- isolating structure that defines an ablation study.
In a clinical context specifically, this ablation approach is also essential for regulatory and interpretability purposes - demonstrating that a diagnostic claim rests on genuine cross-modal signal, not a spurious correlation from a single dominant input.
Reference: Multimodal Data / Experimentation domains - ablation studies for validating fusion architecture design.
NEW QUESTION # 41
You are working with a transformer-based multimodal model that processes both text and audio. You want to implement an efficient attention mechanism that reduces the computational cost associated with attending to the entire input sequence. Which of the following attention mechanisms would be MOST suitable for achieving this goal?
Answer: B
Explanation:
Sparse attention is designed to reduce the computational cost of attention by only attending to a subset of the input sequence at each position. This can significantly reduce the memory and computational requirements, making it suitable for long sequences in multimodal tasks. Other attention mechanisms (A, B, D, E) still require computing attention scores for all or most of the input sequence.
NEW QUESTION # 42
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