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NVIDIA NCA-GENM Exam Syllabus Topics:

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

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NVIDIA Generative AI Multimodal Sample Questions (Q24-Q29):

NEW QUESTION # 24
Which of the following evaluation metrics is MOST appropriate for assessing the performance of a multimodal generative A1 model that generates image captions based on images and audio descriptions?

A. BLEU (Bilingual Evaluation Understudy)
B. Inception Score
C. Root Mean Squared Error (RMSE)
D. Mean Squared Error (MSE)
E. Perplexity

Answer: A

Explanation:
BLEU (B) is specifically designed to evaluate the quality of generated text by comparing it to reference captions. Perplexity is more suitable for language models, Inception Score for generated images, and MSE/RMSE for regression tasks.

NEW QUESTION # 25
Which of the following loss functions is MOST suitable for training a multimodal model for cross-modal retrieval, where the goal is to retrieve relevant images given a text query and vice versa?

A. KL Divergence.
B. Cross-entropy loss.
C. Mean Squared Error (MSE) loss.
D. Binary Cross-entropy loss.
E. Triplet loss.

Answer: E

Explanation:
Triplet loss is specifically designed for learning embeddings where similar items are close together and dissimilar items are far apart. In cross-modal retrieval, you want the embeddings of a text query and its relevant images to be close, while the embeddings of the query and irrelevant images are far apart. Cross-entropy and binary cross-entropy are for classification. MSE is for regression. KL Divergence measures the difference between probability distributions, less suitable here.

NEW QUESTION # 26
You are tasked with optimizing a multimodal model that combines audio and text data for speech recognition. The model currently struggles with noisy audio environments. Which data augmentation technique would be MOST effective in improving the model's robustness to noise?

A. Randomly masking parts of the text input.
B. Translating the text into different languages and back.
C. Rotating the images used for visual context.
D. Normalizing the text data to lowercase.
E. Adding Gaussian noise to the audio data.

Answer: E

Explanation:
Adding Gaussian noise to the audio data directly simulates noisy environments, making the model more robust to such conditions. Randomly masking parts of the text input is a technique used for language modeling, and rotating images is irrelevant to audio processing. Translating the text into different languages and back is not a direct solution to noise in audio. Normalizing the text data to lowercase is more about standardization than noise robustness.

NEW QUESTION # 27
You have trained a multimodal model to generate descriptions for recipes, using images of the finished dish as one modality and a list of ingredients as another. When evaluating the generated descriptions, you notice the descriptions are factually correct in terms of ingredients but often fail to capture the stylistic nuances or tone of professionally written recipes. Which evaluation strategy would provide the most insightful feedback on this aspect of the model's performance?

A. Measure the perplexity of the generated descriptions using a language model trained on a corpus of recipe books.
B. Conduct a human evaluation where individuals rate the generated descriptions based on factors such as informativeness, clarity, and stylistic appropriateness.
C. Calculate the BLEU score between the generated descriptions and a set of reference recipes.
D. Use a pre-trained language model to assess the fluency and coherence of the generated descriptions.
E. Compute the ROUGE score between the generated descriptions and a set of reference recipes.

Answer: B

Explanation:
Human evaluation is the most reliable way to assess subjective aspects of the generated text, such as stylistic appropriateness. While automated metrics like BLEU, ROUGE, and perplexity can provide insights into factual accuracy and fluency, they do not capture the nuances of style and tone as effectively as human judgment. Therefore, the correct answer is (D).

NEW QUESTION # 28
Consider a scenario where you are developing a multimodal model for medical diagnosis using patient medical history (text), X-ray images, and ECG data (time-series). A significant portion of the ECG data is missing due to sensor malfunction. Which of the following approaches would be MOST effective in handling the missing data and ensuring accurate diagnosis?

A. Employ a multimodal fusion technique that is robust to missing modalities, such as attention mechanisms that dynamically weight the available data sources.
B. Combine imputation of missing ECG data with a robust multimodal fusion technique.
C. Train a separate model using only the available medical history and X-ray images, ignoring the ECG data altogether.
D. Replace the missing ECG data with the average values from the entire dataset.
E. Impute the missing ECG values using time-series imputation techniques (e.g., Kalman filtering or interpolation).

Answer: B

Explanation:
Combining imputation with robust fusion is optimal. Imputation recovers some information from the missing data, while robust fusion ensures the model can still make accurate predictions even if the imputed data is not perfect. Ignoring the ECG data or simply replacing it with average values would likely lead to inaccurate diagnoses.

NEW QUESTION # 29
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