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NVIDIA NCA-GENM Exam Syllabus Topics:

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

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NVIDIA Generative AI Multimodal Sample Questions (Q46-Q51):

NEW QUESTION # 46
Consider a scenario where you are developing a multimodal A1 system to translate sign language videos into text. The system utilizes a CNN for processing video frames and an RNN for generating the text sequence. During evaluation, you observe that the system struggles to accurately translate signs that involve complex hand movements or subtle facial expressions. What are the MOST effective strategies to improve performance in this specific scenario? (Select TWO)

A. Increase the depth of the CNN to capture more fine-grained visual features.
B. Augment the training data with variations of the sign language videos, including different camera angles, lighting conditions, and signers.
C. Incorporate a 3D CNN architecture to better capture spatial-temporal information from the video.
D. Replace the RNN with a simpler feed forward neural network for faster processing.
E. Reduce the frame rate of the input video to decrease computational load.

Answer: B,C

Explanation:
3D CNNs are designed to capture spatial-temporal information, which is essential for recognizing complex hand movements. Data augmentation helps the model generalize to different conditions and variations in signing style. Reducing frame rate (A) would likely worsen performance. Replacing the RNN with a feed forward network (D) would remove the ability to model sequential information, which is critical for translation. Increasing the depth of the CNN (B) might help, but a 3D CNN is more directly suited to the task.

NEW QUESTION # 47
Consider the following PyTorch code snippet used for training a Generative A1 model:

A. The model parameters will not be updated correctly since optimizer.step() is called outside the loop.
B. The code is correct and will train the model efficiently.
C. The code will run, but it's computationally inefficient. Gradients should be zeroed before each backward pass.
D. CUDAOOM error because gradients are accumulating without updating parameters.
E. The learning rate scheduler is not being used correctly.

Answer: A,D

Explanation:

The code has two critical issues. First, 'optimizer.step()' is called only once per epoch after accumulating gradients from all batches. This is incorrect, as parameters aren't updated batch-wise. Second, is also called only once per epoch, meaning gradients from all batches accumulate. This will likely lead to a CUDAOOM error, especially for larger models.

NEW QUESTION # 48
You are developing a ML model for image classification. You have a dataset with 10,000 images of cats, dogs and birds. Which of the following ML models would be the most appropriate choice for this task?

A. K-Means Clustering
B. Logistic Regression
C. Convolutional Neural Network (CNN)
D. Linear Regression

Answer: C

Explanation:
CNNs are the standard architecture for image classification because their convolutional layers exploit the spatial locality and translation invariance inherent to image data: learned filters detect local patterns (edges, textures, shapes) that compose hierarchically into higher-level features (parts, objects) as depth increases, without requiring the manual feature engineering that traditional models would need to reach comparable accuracy on raw pixel data. Pooling layers further provide a degree of spatial invariance, and parameter sharing across the image keeps the model tractable relative to a fully connected network operating on raw pixels.
Logistic Regression (A) is a linear classifier that operates on flattened feature vectors; applied directly to raw pixels of a 3-class image problem, it cannot capture the non-linear spatial structure needed to separate cats, dogs, and birds reliably, though it could serve as a baseline or as the final classification head atop CNN- extracted features. K-Means (B) is an unsupervised clustering algorithm - inappropriate here because the task is supervised classification with labeled classes. Linear Regression (C) predicts continuous outputs and is not designed for categorical class prediction at all.
For 10,000 labeled images, a CNN (potentially fine-tuned from a pretrained backbone via transfer learning, given the modest dataset size) is the appropriate and industry-standard choice.
Reference: Core Machine Learning and AI Knowledge domain - CNN architecture for image classification.

NEW QUESTION # 49
You have a dataset of customer reviews for a Generative A1 service. The dataset contains text reviews, numerical ratings (1-5 stars), and categorical data about the customer's subscription plan (Basic, Premium, Enterprise). You want to build a model to predict the numerical rating based on the text review and subscription plan. Which data analysis and modeling approach would be MOST suitable?

A. Use topic modeling on the text reviews, then use logistic regression to predict the numerical rating based on the topic distributions and subscription plan.
B. Calculate the average word length of the text reviews and use that as a feature in a linear regression model along with the subscription plan to predict the rating.
C. Use a decision tree to predict the numerical rating based on the text reviews (using TF-IDF) and subscription plan.
D. Perform sentiment analysis on the text reviews, then use linear regression to predict the numerical rating based on the sentiment score and subscription plan (one-hot encoded).
E. Train a deep learning model (e.g., BERT or RoBERTa) on the text reviews, concatenate the output embeddings with the one-hot encoded subscription plan, and use a regression layer to predict the numerical rating.

Answer: E

Explanation:
Using a pre-trained language model like BERT or RoBERTa captures the semantic meaning of the text reviews most effectively. Concatenating the embeddings with the subscription plan allows the model to learn the combined effect of both inputs. Regression layer is used as numeric ratings (1-5 stars) are provided as the target values. Sentiment and topic modeling can work as features but BERT/RoBERTa gives better context. Other options aren't able to capture complex context.

NEW QUESTION # 50
You are working on a multimodal AI model that generates images from text descriptions. You notice that the generated images often lack fine-grained details and appear blurry. Which of the following techniques is LEAST likely to improve the visual quality of the generated images?

A. Increasing the latent space dimensionality of the generative model.
B. Using a perceptual loss function that penalizes differences in high-level features.
C. Reducing the batch size during training.
D. Employing a convolutional neural network (CNN) with strided convolutions for upsampling.
E. Training with a larger dataset of higher-resolution images.

Answer: C

Explanation:
Reducing the batch size during training often leads to more unstable training and can actually worsen the quality of generated images, especially in generative adversarial networks (GANs) or similar models. The other options (A, B, C, E) are all techniques commonly used to improve the detail and sharpness of generated images. Larger latent space allows more nuanced representations. Perceptual losses encourage the model to generate realistic-looking images. Improved upsampling avoids checkerboard artifacts. More high-res data helps.

NEW QUESTION # 51
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