NCA-GENM資格試験、NCA-GENM必殺問題集

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NVIDIA NCA-GENM Exam Syllabus Topics:

Section	Objectives
Generative AI Concepts	- Generative models 1. Transformers and LLM basics 2. Diffusion models
Core AI and Machine Learning Fundamentals	- Machine learning basics 1. Neural networks fundamentals 2. Supervised and unsupervised learning
Multimodal AI Systems	- Cross-modal learning 1. Text-image integration 2. Audio-visual understanding - Multimodal model design
Responsible and Trustworthy AI	- Bias and safety considerations - Ethical AI principles
NVIDIA AI Ecosystem	- NVIDIA tools and frameworks 1. NeMo framework usage 2. GPU-accelerated AI workflows

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NCA-GENM必殺問題集 & NCA-GENM最新日本語版参考書

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NVIDIA Generative AI Multimodal 認定 NCA-GENM 試験問題 (Q25-Q30):

質問 # 25
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?

A. Using a robust statistics-based normalization technique (e.g., Z-score normalization with median and interquartile range).
B. Converting all images to grayscale.
C. Clipping pixel values to a specific range (e.g., [0, 255]).
D. Applying a Gaussian blur to all images.
E. Applying histogram equalization to all images.

正解：A

解説：
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.

質問 # 26
You are tasked with optimizing a multimodal A1 model that processes both images and text. You observe significant latency during the image encoding phase using a pre-trained ResNet50 model. Which of the following techniques would be MOST effective in reducing latency while preserving accuracy, considering energy efficiency?

A. Use full precision floating point operations throughout the ResNet50 model.
B. Disable GPU acceleration for image processing to reduce power consumption.
C. Increase the batch size for image processing.
D. Replace ResNet50 with a larger, more complex model like ResNeXt101.
E. Apply knowledge distillation, training a smaller, faster model to mimic the ResNet50 output.

正解：E

解説：
Knowledge distillation involves training a smaller, more efficient model to approximate the behavior of a larger, more accurate model. This can significantly reduce latency without a major drop in accuracy. Increasing batch size (A) may increase throughput but doesn't necessarily reduce latency per image. Replacing with a larger model (C) will increase latency and power consumption. Using full precision (D) is less energy-efficient than using mixed precision or quantization. Disabling GPU acceleration (E) would drastically increase latency.

質問 # 27
You are using a pre-trained language model for text classification. You observe that the model performs well on the training data but poorly on unseen dat a. Which of the following techniques could help improve the model's generalization ability? (Select TWO)

A. Decreasing batch size.
B. Using weight decay (L2 regularization).
C. Applying data augmentation techniques (e.g., random synonym replacement, back-translation).
D. Increasing the learning rate.
E. Decreasing the amount of training data.

正解：B、C

解説：
Weight decay penalizes large weights, preventing the model from overfitting to the training data. Data augmentation increases the diversity of the training data, making the model more robust to variations in unseen data. Increasing the learning rate can lead to instability and overfitting. Decreasing the amount of training data reduces the model's ability to learn general patterns.

質問 # 28
You are developing a multimodal system for medical diagnosis using MRI images and patient history text. Your initial model performs poorly on patients with rare conditions. Which of the following data augmentation techniques would be MOST effective in improving the model's performance on these under-represented cases?

A. Back-translating the patient history text to generate paraphrased versions-
B. Randomly cropping and rotating MRI images.
C. Adding Gaussian noise to the pixel values of MRI images.
D. Using a Generative Adversarial Network (GAN) to synthesize new MRI images conditioned on the patient history text, specifically targeting the rare conditions-
E. Replacing named entities in the patient history text with other random entities-

正解：D

解説：
Using a GAN to synthesize new data specifically for rare conditions is the most effective option. It directly addresses the class imbalance problem by creating more examples of the under-represented cases, conditioned on both the image and text modalities. While random image augmentations (AD) and text paraphrasing (B) can help, they don't directly target the rare conditions. Entity replacement could change the meaning of the history.

質問 # 29
You are training a text-to-image diffusion model and observe that the generated images often exhibit a 'washed-out' or overly smooth appearance. Which of the following adjustments to the training process would likely improve the image quality and detail?

A. Decrease the number of diffusion steps used during training.
B. Increase the weight of the perceptual loss function in the training objective.
C. Apply more aggressive data augmentation techniques to the training dataset.
D. Reduce the batch size used during training to minimize memory consumption.
E. Reduce the learning rate for the U-Net architecture within the diffusion model.

正解：B

解説：
A perceptual loss function encourages the generated images to have more realistic features and details, as it compares the high- level representations of the generated images to the real images. Increasing its weight in the training objective would incentivize the model to produce more detailed and visually appealing results. Decreasing diffusion steps leads to faster but often lower-quality results. Reducing batch size can affect training stability but doesn't directly address the 'washed-out' appearance. Data augmentation and learning rate adjustments may have some impact, but are less directly targeted at improving image detail.

質問 # 30
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