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| Section | Objectives |
|---|---|
| Generative AI Concepts | - Generative models
|
| NVIDIA AI Ecosystem | - NVIDIA tools and frameworks
|
| Multimodal AI Systems | - Cross-modal learning
|
| Core AI and Machine Learning Fundamentals | - Machine learning basics
|
| Responsible and Trustworthy AI | - Ethical AI principles - Bias and safety considerations |
>> Valid NCA-GENM Exam Topics <<
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NEW QUESTION # 45
Consider a scenario where you are building a multimodal model to generate realistic indoor scenes. You have access to text descriptions of the scene, 3D models of furniture, and ambient sound recordings. Which of the following loss functions would be most appropriate to ensure coherence and realism in the generated scenes?
Answer: E
Explanation:
A combination of adversarial loss, perceptual loss, and semantic consistency loss provides the best approach. Adversarial loss enhances realism, perceptual loss focuses on high-level feature matching, and semantic consistency loss aligns the generated image with the input text, ensuring a coherent and realistic scene.
NEW QUESTION # 46
You are working on a generative A1 model that creates descriptions of images. During experimentation, you notice the model consistently generates descriptions that are factually incorrect about objects in the image, despite the image quality being high. For example, it might describe a 'cat' as a 'dog'. What is the MOST critical step to address this issue?
Answer: E
Explanation:
Factually incorrect descriptions indicate a lack of grounding in real-world knowledge. Verifying against an external knowledge base (B) directly addresses this issue. Increasing data size (A) might help, but it's not guaranteed. Fine-tuning (C) and increasing model complexity (D) might not solve the grounding problem. Image sharpening (E) is irrelevant to factual accuracy.
NEW QUESTION # 47
You are tasked with evaluating the scalability of a multimodal generative model deployed on an NVIDIAAI 00 GPU. The model processes text, images, and audio. Which of the following metrics and tools would be MOST relevant to monitor and analyze?
Answer: B,C
Explanation:
GPU utilization, GPU memory usage, and throughput (samples per second) are crucial for assessing GPU workload and processing speed. CUDA and Tensor Core utilizations show how effectively the NVIDIA GPU's parallel processing capabilities are being used. While CPU and network performance can be bottlenecks, the GPU is the primary resource to evaluate for model scalability. Disk 1/0 is relevant for large datasets but less so for real-time inference.
NEW QUESTION # 48
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: B
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 # 49
You're using a diffusion model to generate high-resolution images. You notice that the generated images often contain artifacts and inconsistencies. Which of the following techniques could help improve the image quality?
Answer: B,C
Explanation:
Increasing the number of diffusion steps allows the model to gradually refine the image and reduce artifacts. Classifier-free guidance provides a way to control the generation process and improve image quality by conditioning on a specific class or attribute. Training with a larger batch size may improve training stability but doesn't directly address artifact reduction. A smaller image size will reduce computational cost but doesn't necessarily improve quality at the desired resolution. Decreasing the number of diffusion steps can lead to lower-quality images with more artifacts.
NEW QUESTION # 50
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