NCA-GENM Valid Test Syllabus - NCA-GENM Learning Engine

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NVIDIA NCA-GENM Exam Syllabus Topics:

Section	Weight	Objectives
Core ML & AI Knowledge	20%	- Basic concepts and terminology - Key algorithms and techniques
Trustworthy AI	5%	- Ensuring fairness and transparency - Ethical considerations in AI development
Data Analysis & Visualization	10%	- Data preprocessing and feature engineering - Visualization techniques for multimodal data
Software Development & Engineering	15%	- Python libraries for multimodal AI - Integration and deployment of multimodal AI systems
Multimodal Data	15%	- Handling and integrating text, image, and audio data - Applications and use cases
Performance Optimization	10%	- Monitoring and improving system efficiency - Techniques for optimizing AI performance
Experimentation	25%	- Hypothesis testing - Model evaluation and comparison - A/B testing - Experimental design

>> NCA-GENM Valid Test Syllabus <<

Quiz 2026 NCA-GENM: Updated NVIDIA Generative AI Multimodal Valid Test Syllabus

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NVIDIA Generative AI Multimodal Sample Questions (Q55-Q60):

NEW QUESTION # 55
Consider the following code snippet which aims to create a custom prompt for a Stable Diffusion model using the 'diffusers* library. The goal is to generate an image of 'a cat wearing a hat sitting on a chair'. Which of the following modifications would MOST effectively improve the quality and coherence of the generated image?

A. Using a smaller image resolution to reduce computational cost.
B. Increasing the 'guidance_scale' and adding a negative prompt such as 'blurry, distorted'.
C. Decreasing the number of inference steps to speed up the generation process.
D. Removing the word 'sitting' from the prompt to make it more general.
E. Adding more unrelated keywords to the prompt to increase diversity.

Answer: B

Explanation:
Increasing the 'guidance_scales forces the generated image to adhere more closely to the prompt. Adding a negative prompt helps remove unwanted artifacts and improves image quality. Negative prompt is a well know prompt engginerring technique to remove unwanted objects or attributes.

NEW QUESTION # 56
You're working on a multimodal A1 model that combines audio and text to generate music. You notice that the generated music lacks musical structure and sounds random. Which of the following techniques could be applied to improve the coherence and musicality of the generated output?

A. Using a Variational Autoencoder (VAE) to learn a latent representation of musical structure.
B. Increasing the size of the model's hidden layers.
C. Training the model on a larger dataset of music.
D. Using a Recurrent Neural Network (RNN) with attention mechanism to model sequential dependencies in the music.
E. Adding more layers to the model.

Answer: A,D

Explanation:
A VAE can learn a structured latent space that captures essential musical features, allowing for controlled generation. RNNs with attention are well-suited for modeling sequential data like music, capturing long-range dependencies and creating a more coherent structure. Simply increasing the size or depth of the model may not address the underlying issue of musical structure. A larger dataset may help, but structured modeling techniques are generally more effective.

NEW QUESTION # 57
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 Transformer-based architecture with separate encoders for image and text, followed by a decoder that generates video frames.
C. A simple recurrent neural network (RNN) that concatenates image feature vectors and text embeddings as input at each time step.
D. A Support Vector Machine (SVM) classifier trained to predict the next frame based on image and text features.
E. A Generative Adversarial Network (GAN) trained solely on image data and later fine-tuned with text embeddings.

Answer: B

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 # 58
For building a zero-shot image classification pipeline, what could be a crucial step in the process?

A. Focusing on enhancing the resolution and quality of images before classification.
B. Designing an algorithm to replace the need for textual descriptions in the classification process.
C. Using a model like CLIP for encoding both images and their textual descriptions into a shared representation space for comparison.
D. Manually labeling each image in the dataset for precise classification.

Answer: C

Explanation:
Zero-shot image classification, by definition, requires classifying images into categories the model was never explicitly trained to recognize, with no task-specific labeled examples. CLIP-style models enable this by encoding both images and candidate text labels (e.g., "a photo of a {class}") into a shared embedding space; classification then reduces to a similarity comparison - computing cosine similarity between the image embedding and each candidate text embedding and selecting the closest match. This is the crucial architectural step: without a shared embedding space linking visual and textual semantics, there is no mechanism to generalize to unseen classes using only their names or descriptions.
Option B directly contradicts the "zero-shot" premise - manual labeling of the target dataset is precisely what zero-shot classification is designed to avoid; if labels were being collected for the target classes, the task would be standard supervised classification, not zero-shot. Option A (image enhancement) may marginally help downstream accuracy but is not the crucial, defining step. Option D is incoherent with how CLIP-style zero-shot classification actually works - the textual description of each candidate class is the essential input that makes zero-shot generalization possible; eliminating it would remove the mechanism entirely, not improve it.
Reference: Multimodal Data domain - zero-shot classification via shared embedding spaces (CLIP).

NEW QUESTION # 59
You want to ensure that the system respects user privacy and avoids generating avatars that resemble real people without their consent.
Which of the following strategies would be MOST effective in addressing this ethical concern?

A. Implementing a face recognition system to detect and filter out avatars that closely resemble existing individuals.
B. Training the model on a dataset that includes images of real people with explicit consent.
C. Adding watermarks to all generated avatars to indicate that they are A1-generated.to global minimum.
D. Generating all avatars with a cartoonish or stylized appearance to avoid any resemblance to real people.
E. Using a diverse and anonymized dataset for training and adding a mechanism to prevent the model from generating highly realistic facial features.

Answer: E

Explanation:
Using a diverse and anonymized dataset reduces the risk of the model learning to generate avatars that are too similar to specific individuals. Preventing the generation of highly realistic facial features further mitigates this risk while still allowing for personalized avatars. Ethical concerns are paramount when developing generative A1 systems, particularly when dealing with potentially sensitive data such as facial features.

NEW QUESTION # 60
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