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NVIDIA NCA-GENM Exam Syllabus Topics:

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

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NVIDIA Generative AI Multimodal Sample Questions (Q52-Q57):

NEW QUESTION # 52
Which of the following are valid techniques for improving the trustworthiness of a Generative A1 model used for generating product descriptions based on images, specifically addressing potential biases and ensuring fairness? (Select TWO)

A. Employing adversarial debiasing techniques during training to mitigate biases learned from the training data.
B. Using only images of products from a single manufacturer for training.
C. Actively curating a diverse and representative training dataset that reflects the real-world distribution of products and avoiding over-representation of specific categories.
D. Implementing a robust input validation and filtering mechanism to remove potentially harmful or biased inputs.
E. Ignoring fairness metrics during model evaluation to focus solely on overall accuracy.

Answer: A,C

Explanation:
Adversarial debiasing aims to make the model invariant to sensitive attributes, thereby reducing bias. A diverse and representative training dataset is crucial for fairness as it avoids skewed learning. Using data from a single manufacturer (A) introduces bias. Ignoring fairness metrics (D) defeats the purpose of ensuring trustworthiness. Input validation (B) is important for safety, but doesn't directly address inherent biases in the model's learned representations.

NEW QUESTION # 53
You are training a multimodal Generative A1 model for generating video captions. The model is overfitting to the training data, resulting in poor generalization to unseen videos. Which of the following regularization techniques would be MOST suitable to mitigate overfitting?

A. Data augmentation (e.g., adding noise to the video frames)
B. Batch normalization
C. Early stopping based on a validation set
D. L1 regularization on the model weights
E. Dropout

Answer: A,C,D,E

Explanation:
Dropout randomly deactivates neurons during training, preventing the model from relying on specific neurons. L1 regularization adds a penalty to the loss function based on the absolute values of the weights, encouraging sparsity. Data augmentation increases the diversity of the training data. Early stopping prevents the model from training for too long and overfitting. Batch normalization primarily improves training stability and speed but also provides some regularization.

NEW QUESTION # 54
You are fine-tuning a large pre-trained language model for a specific downstream task using a limited amount of training dat a. Which of the following techniques is MOST likely to prevent overfitting and improve the model's generalization performance?

A. Removing all regularization techniques to allow the model to perfectly fit the training data.
B. Using a very large learning rate during fine-tuning.
C. Increasing the batch size as much as possible to maximize GPU utilization.
D. Training the entire model from scratch using the limited training data.
E. Applying aggressive weight decay and dropout regularization.

Answer: E

Explanation:
Overfitting occurs when a model learns the training data too well and fails to generalize to unseen data. Aggressive weight decay and dropout are regularization techniques that penalize complex models and prevent them from memorizing the training data. Training from scratch with limited data will almost certainly lead to overfitting. A large learning rate can also exacerbate overfitting. While a larger batch size can improve training efficiency, it doesn't directly address overfitting.

NEW QUESTION # 55
You are tasked with building a Generative A1 model that can generate realistic images of birds based on text descriptions. You have a large dataset of bird images and corresponding text captions. Which of the following architectures is MOST suitable for this task, considering both image quality and training efficiency?

A. An Image Transformer model trained from scratch.
B. A standard Convolutional Neural Network (CNN) for image generation.
C. A Variational Autoencoder (VAE) trained on the image dataset.
D. A Generative Adversarial Network (GAN) conditioned on the text descriptions (e.g., a StackGAN or AttnGAN).
E. A simple Recurrent Neural Network (RNN) to generate pixel values sequentially.

Answer: D

Explanation:
GANs, especially those conditioned on text descriptions, are specifically designed for generating realistic images based on textual input. StackGAN and AttnGAN are examples that have shown good performance. CNNs and VAEs are less effective for high-quality image generation from text. RNNs are not well-suited for generating images pixel by pixel. Image transformers are possible, but computationally expensive for training from scratch on a large dataset like this, although diffusion models based on transformers are becoming more popular

NEW QUESTION # 56
Consider the following code snippet intended to generate an image embedding using CLIP. What is the most likely reason for the 'RuntimeErroN?

A. The image pixel values are not normalized correctly.
B. The image tensor does not require gradient calculation.
C. The image is not in RGB format.
D. The image size is not compatible with the CLIP model's input requirements.
E. The CLIP model was not properly loaded onto the GPIJ.

Answer: D

Explanation:
CLIP models typically require images to be resized to a specific dimension (e.g., 224x224). The 'RuntimeError' suggests a size mismatch. The provided code snippet, though not complete, doesn't explicitly resize the image before passing it to the model.

NEW QUESTION # 57
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