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NVIDIA NCA-GENM Exam Syllabus Topics:

Section	Weight	Objectives
Software Development and Engineering	15%	- Development workflows for generative AI applications - Best practices for building and maintaining systems - Libraries, frameworks, and tools for multimodal AI
Multimodal Data	15%	- Characteristics of text, image, and audio data - Data preprocessing, fusion, and representation - Multimodal model architectures and integration
Performance Optimization	10%	- Hardware acceleration with NVIDIA platforms - Model efficiency and inference optimization - Scalability and deployment considerations
Trustworthy AI	5%	- Robustness and error mitigation - Ethical considerations and responsible use - Reliability, fairness, and safety in generative systems
Experimentation	25%	- Experiment design and methodology - Model training, fine-tuning, and evaluation - Metrics and validation strategies for generative models
Core Machine Learning and AI Knowledge	20%	- Neural network architectures relevant to multimodal systems - Generative AI principles and techniques - Fundamental concepts of machine learning and deep learning
Data Analysis and Visualization	10%	- Visualization techniques for model behavior and results - Analyzing multimodal datasets and outputs - Interpretation of generative AI outputs

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NVIDIA Generative AI Multimodal Sample Questions (Q35-Q40):

NEW QUESTION # 35
You have developed a multimodal generative A1 model that generates images based on textual descriptions. You want to set up an automated system to monitor the model's performance and identify potential issues like degradation in image quality or introduction of biases over time. Which of the following components are essential for such a monitoring system? (Select THREE)

A. A database to store all training data used for the model.
B. Automated metrics calculation pipelines to continuously compute relevant metrics like FID, CLIP score, and bias detection metrics on generated images.
C. A mechanism to store and analyze the text prompts used to generate the images.
D. Alerting mechanisms that trigger notifications when performance metrics fall below predefined thresholds or when biases are detected.
E. A web interface for users to manually upload images and rate the generated images.

Answer: B,C,D

Explanation:
Automated metrics calculation (B) and alerting mechanisms (C) are crucial for continuously monitoring performance and detecting issues. Storing and analyzing text prompts (E) can help identify patterns related to performance degradation or bias. While storing training data (A) can be useful, it's not essential for monitoring. A manual evaluation interface (D) can be helpful for occasional spot-checks, but it's not a core component of an automated monitoring system.

NEW QUESTION # 36
Which of the following techniques are commonly used to address the 'hallucination' problem in generative A1 models, where the model generates content that is factually incorrect or nonsensical? (Select all that apply)

A. Lowering the temperature parameter during decoding to encourage more conservative and predictable outputs.
B. Employing adversarial training techniques to improve the model's robustness to noisy input data.
C. Using Reinforcement Learning from Human Feedback (RLHF) to align the model's output with human preferences and factual correctness.
D. Implementing retrieval-augmented generation (RAG), where the model consults an external knowledge source before generating content.
E. Increasing the model size and training dataset.

Answer: A,C,D

Explanation:
RLHF helps align the model with human values, reducing nonsensical outputs. RAG grounds the generation process in external knowledge, ensuring factual accuracy. Lowering the temperature reduces the likelihood of sampling low-probability, potentially hallucinated, tokens. Increasing model Size or using adversarial training can improve model performance, but doesn't directly address hallucination.

NEW QUESTION # 37
Consider the following Python code snippet using PyTorch, intended to combine image and text embeddings:

Which of the following statements regarding the output shapes of these combined embeddings are TRUE? (Select TWO)

A. combined_embedding_concat has shape (64, 512).
B. combined_embedding_add has shape (32, 1024).
C. combined_embedding_weighted has shape (32, 512).
D. combined_embedding_concat has shape (32, 1024).
E. combined_embedding_weighted has shape (32, 1024).

Answer: C,D

Explanation:
torch.cat concatenates the embeddings along dimension 1, resulting in shape (32, 1024). Element-wise addition maintains the original shape (32, 512). The weighted sum is also element-wise, preserving the (32, 512) shape.

NEW QUESTION # 38
You are fine-tuning a pre-trained large language model (LLM) for a specific text generation task using LoRA (Low-Rank Adaptation).
Which of the following statements accurately describes the benefits and limitations of using LoRA?

A. A and B.
B. LoRA reduces the number of trainable parameters by inserting low-rank matrices into the original model layers, making fine-tuning more memory-efficient.
C. LoRA can improve the accuracy of the fine-tuned model compared to full fine-tuning by preventing overfitting.
D. LoRA is not compatible with model parallelism techniques.
E. LoRA allows for efficient task switching by only storing and loading the small LoRA parameters for different tasks, while keeping the original LLM weights frozen.

Answer: A

Explanation:
LoRA significantly reduces the number of trainable parameters, enabling more memory-efficient fine-tuning, especially for large models. It also facilitates efficient task switching as only the small LoRA parameters need to be stored and loaded for different tasks. While LoRA can help prevent overfitting compared to full fine-tuning, it doesn't guarantee improved accuracy. LoRA can be effectively combined with model parallelism.

NEW QUESTION # 39
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. Decreasing the number of inference steps to speed up the generation process.
B. Removing the word 'sitting' from the prompt to make it more general.
C. Adding more unrelated keywords to the prompt to increase diversity.
D. Using a smaller image resolution to reduce computational cost.
E. Increasing the 'guidance_scale' and adding a negative prompt such as 'blurry, distorted'.

Answer: E

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 # 40
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