NCA-GENM Latest Exam Format & NCA-GENM Latest Practice Questions

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NVIDIA NCA-GENM Exam Syllabus Topics:

Section	Objectives
Topic 1: NVIDIA AI Ecosystem	- NVIDIA tools and frameworks 1. NeMo framework usage 2. GPU-accelerated AI workflows
Topic 2: Generative AI Concepts	- Generative models 1. Diffusion models 2. Transformers and LLM basics
Topic 3: Multimodal AI Systems	- Cross-modal learning 1. Text-image integration 2. Audio-visual understanding - Multimodal model design
Topic 4: Responsible and Trustworthy AI	- Ethical AI principles - Bias and safety considerations
Topic 5: Core AI and Machine Learning Fundamentals	- Machine learning basics 1. Neural networks fundamentals 2. Supervised and unsupervised learning

>> NCA-GENM Latest Exam Format <<

Free PDF NVIDIA - NCA-GENM –Professional Latest Exam Format

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NVIDIA Generative AI Multimodal Sample Questions (Q15-Q20):

NEW QUESTION # 15
You're building a multimodal model to predict stock prices using news articles (text) and historical price data (time series). You observe that the model performs well on the training data but poorly on unseen dat a. Which of the following techniques would be MOST effective in addressing this issue?

A. All of the above
B. Add L1 or L2 regularization to the model's parameters.
C. Implement early stopping based on the performance of the model on a validation set.
D. Use data augmentation techniques to generate more diverse variations of the existing data.
E. Increase the size of the training dataset by collecting more news articles and historical price data.

Answer: C

Explanation:
All the listed techniques combat overfitting. Increasing data size provides more examples. Regularization penalizes complex models. Early stopping prevents training too long on the training data. Data augmentation creates more robust and generalizable model by artifically expanding data through techniques like Tme series perturbation and Text paraphrasing.

NEW QUESTION # 16
You're tasked with building a system that can generate realistic images from text descriptions and, conversely, generate accurate text descriptions from images. You decide to use a GAN (Generative Adversarial Network) architecture, but need to handle both modalities effectively. What GAN variant would be MOST suitable for this bi-directional multimodal task?

A. CycleGAN
B. Super-Resolution GAN (SRGAN)
C. Conditional GAN (cGAN)
D. Deep Convolutional GAN (DCGAN)
E. Vanilla GAN

Answer: A

Explanation:
CycleGAN is designed for unpaired image-to-image translation. In this scenario, it can be adapted to translate between the image and text modalities without requiring paired data. One generator learns to generate images from text, while another learns to generate text from images. Cycle consistency ensures that translating an image to text and then back to an image results in an image similar to the original. Vanilla GANI cGAN, and DCGAN are not inherently designed for bi- directional translation between modalities without paired data. SRGAN is for image super-resolution.

NEW QUESTION # 17
You are tasked with building a multimodal generative A1 model that takes both image and text as input to generate a coherent video. Which of the following architectures is MOST suitable for this task, considering the need to fuse information from different modalities and generate sequential data?

A. A standard Convolutional Neural Network (CNN) followed by a fully connected layer.
B. A Support Vector Machine (SVM) classifier trained to predict the next frame based on image and text features.
C. A Transformer-based architecture with separate encoders for image and text, followed by a decoder that generates video frames.
D. A simple recurrent neural network (RNN) that concatenates image feature vectors and text embeddings as input at each time step.
E. A Generative Adversarial Network (GAN) trained solely on image data and later fine-tuned with text embeddings.

Answer: C

Explanation:
Transformer-based architectures are well-suited for multimodal tasks as they can effectively fuse information from different modalities through attention mechanisms. The separate encoders can handle image and text data, and the decoder can generate the video frames sequentially CNNs and RNNs alone may struggle with long-range dependencies, and GANs might not directly incorporate textual information during video generation. SVMs are classifiers, not generative models.

NEW QUESTION # 18
You are developing a text-to-image generative model and want to evaluate the quality and diversity of the generated images. Which metric is MOST appropriate for assessing the diversity of generated images, considering computational efficiency is also important?

A. Learned Perceptual Image Patch Similarity (LPIPS)
B. Inception Score (IS)
C. Number of unique images in the generated set.
D. Frechet Inception Distance (FID)
E. Multi-Scale Structural Similarity Index (MS-SSIM)

Answer: D

Explanation:
FID is a commonly used metric for evaluating the quality and diversity of generated images, and it's more robust than IS. While LPIPS and MS-SSIM are useful for measuring perceptual similarity between images, they don't directly quantify diversity across a large set of generated images. Simply counting unique images doesn't capture the semantic diversity of the generated content. FID calculates the distance between the feature representations of real and generated images, capturing both quality and diversity.

NEW QUESTION # 19
You are tasked with optimizing a large multimodal AI model for deployment on edge devices with limited computational resources. Which combination of techniques would provide the BEST trade-off between model accuracy and inference speed? (Select TWO)

A. Increasing the number of attention heads in the transformer architecture.
B. Pruning to remove less important connections in the model.
C. Using larger batch sizes during inference to maximize GPIJ utilization.
D. Adding more layers to the model to increase its representational capacity.
E. Model quantization (e.g., INT8) to reduce model size and improve inference speed.

Answer: B,E

Explanation:
Model quantization reduces the model size and accelerates inference by using lower-precision arithmetic. Pruning reduces the number of parameters, leading to faster computation and lower memory footprint. Increasing attention heads and adding layers increase computational cost. Larger batch sizes can improve GPU utilization on servers, but might not be feasible on resource-constrained edge devices.

NEW QUESTION # 20
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