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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: NVIDIA AI Ecosystem	- NVIDIA tools and frameworks 1. GPU-accelerated AI workflows 2. NeMo framework usage
Topic 4: Core AI and Machine Learning Fundamentals	- Machine learning basics 1. Supervised and unsupervised learning 2. Neural networks fundamentals
Topic 5: Generative AI Concepts	- Generative models 1. Diffusion models 2. Transformers and LLM basics

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NVIDIA Generative AI Multimodal Sample Questions (Q22-Q27):

NEW QUESTION # 22
You're training a model to generate code snippets from natural language descriptions. You are using a Transformer architecture and a large dataset of code examples. You notice the model frequently generates syntactically correct code, but the code doesn't accurately implement the described functionality (i.e., it's semantically incorrect). Select TWO methods which could improve the semantic correctness of the generated code.

A. Use a reinforcement learning approach where the reward is based on whether the generated code passes the unit tests associated with the descriptiom
B. Increase the number of Transformer layers.
C. Fine-tune the model on a subset of the data where each code snippet is accompanied by unit tests, and train the model to also generate these tests.
D. Increase the size of the vocabulary used by the model.
E. Apply techniques like beam search during decoding to generate more diverse code snippets.

Answer: A,C

Explanation:
Fine-tuning with unit tests (B) and using reinforcement learning with unit test rewards (D) directly address semantic correctness- The model learns to generate code that not only compiles but also behaves as expected- Increasing Transformer layers (A) or vocabulary size (E) might improve syntactic correctness or fluency, but don't guarantee semantic accuracy Beam search (C) generates more diverse outputs, but doesn't explicitly guide the model towards semantic correctness.

NEW QUESTION # 23
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. Implementing a robust input validation and filtering mechanism to remove potentially harmful or biased inputs.
B. Employing adversarial debiasing techniques during training to mitigate biases learned from the training data.
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. Using only images of products from a single manufacturer for training.
E. Ignoring fairness metrics during model evaluation to focus solely on overall accuracy.

Answer: B,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 # 24
Consider a scenario where you are developing a system for automatically generating product descriptions based on images and specifications. The system needs to generate diverse and creative descriptions. Which of the following techniques would be MOST helpful in achieving this?

A. Employing a denoising autoencoder to clean the images before feeding them into the description generation model.
B. Fine-tuning a pre-trained language model (e.g., GPT-3) on a dataset of product descriptions, using the image features as a conditional input.
C. Training a recurrent neural network (RNN) from scratch to generate the descriptions.
D. Using a simple template-based approach with predefined sentence structures.
E. Using a rule-based system to extract keywords from the image and specifications and then assemble them into sentences.

Answer: B

Explanation:
Fine-tuning a pre-trained language model (option B) is the most effective approach. Pre-trained language models like GPT-3 have a strong understanding of language and can generate fluent and creative text. Fine-tuning on a dataset of product descriptions, conditioned on image features, allows the model to learn to generate descriptions that are both accurate and engaging. Other options are either too rigid (A, D) or require more training data and computational resources (C).

NEW QUESTION # 25
You are developing an Avatar Cloud Engine (ACE) application for a virtual assistant that needs to generate realistic facial expressions based on user emotions detected from text. Which ACE microservice would be most directly responsible for this functionality?

A. Lip Sync
B. Facial Animation
C. Text to Speech (TTS)
D. speech to Text (STT)
E. Natural Language Understanding (NLU)

Answer: B

Explanation:
The Facial Animation microservice within ACE is specifically designed to generate realistic facial expressions for avatars. While NLU detects the emotion, Facial Animation translates that emotion into corresponding facial movements.

NEW QUESTION # 26
You are working with a large dataset of images for training a generative model. The dataset contains a significant amount of noise and outliers. Which of the following data preprocessing techniques would be MOST effective in mitigating the impact of noise and outliers on the model's performance?

A. Clipping pixel values to a specific range (e.g., [0, 255]).
B. Converting all images to grayscale.
C. Applying a Gaussian blur to all images.
D. Applying histogram equalization to all images.
E. Using a robust statistics-based normalization technique (e.g., Z-score normalization with median and interquartile range).

Answer: E

Explanation:
Robust statistics-based normalization techniques, such as Z-score normalization using the median and interquartile range (IQR), are less sensitive to outliers than traditional methods like mean and standard deviation. Clipping pixel values can help to limit the impact of extreme outliers, but it may also remove valid data. Histogram equalization and Gaussian blur can improve image quality, but they are not specifically designed to handle outliers. Converting to grayscale reduces information but doesn't address noise specifically.

NEW QUESTION # 27
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