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NVIDIA Generative AI Multimodal Sample Questions (Q171-Q176):

NEW QUESTION # 171
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. Using Reinforcement Learning from Human Feedback (RLHF) to align the model's output with human preferences and factual correctness.
B. Increasing the model size and training dataset.
C. Lowering the temperature parameter during decoding to encourage more conservative and predictable outputs.
D. Implementing retrieval-augmented generation (RAG), where the model consults an external knowledge source before generating content.
E. Employing adversarial training techniques to improve the model's robustness to noisy input data.

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 # 172
Consider the following code snippet, where you are trying to load image and text data for a multimodal model. What is the most likely cause of error if the code fails during the image loading step?

A. The batch size is too large.
B. The system doesn't have CUDA drivers installed.
C. The learning rate is set too high.
D. The text data is not in IJTF-8 encoding.
E. The image files are corrupted or in an unsupported format.

Answer: E

Explanation:
Since the error occurs specifically during the image loading step, the most likely cause is related to the image files themselves. Corrupted files or unsupported formats would prevent the image loading library from successfully reading the images. The other options are less likely to cause an error specifically during image loading.

NEW QUESTION # 173
Consider the following code snippet used for creating a multimodal dataset with PyTorch. The dataset contains images and corresponding text descriptions. However, during training, you observe a significant imbalance in the data distribution of text lengths. Which of the following techniques would BEST address this issue?

A. Using the exact same length of text and same images.
B. Applying Batch Normalization to the image features.
C. Using a learning rate scheduler to adjust the learning rate during training-
D. Padding or truncating text sequences to a fixed length.
E. Applying standard image augmentation techniques to the image data.

Answer: D

Explanation:
Padding or truncating text sequences to a fixed length is a standard technique for handling variable-length sequences in NLP tasks. This ensures that all text inputs have the same dimensionality, which is required for efficient batch processing in neural networks- While image augmentation can improve the model's robustness to variations in image data, it does not directly address the issue of text length imbalance. Learning rate scheduling and batch normalization are general training techniques that can improve convergence, but they do not specifically address the text length imbalance.

NEW QUESTION # 174
You're tasked with building a generative A1 model for music composition. You have a large dataset of MIDl files, but the data is inconsistent in terms of tempo, key, and instrumentation. What are the crucial data transformation steps needed before training the model?

A. Standardizing the instrumentation by mapping different instrument patches to a predefined set.
B. Rescaling the MIDl note velocities to a uniform range.
C. Transposing all MIDl files to the same key (e.g., C major/A minor).
D. Converting all MIDl files to MP3 format.
E. Normalizing the tempo of all MIDl files to a standard BPM.

Answer: A,B,C,E

Explanation:
Consistent data is crucial for training a generative model. Rescaling note velocities ensures a uniform dynamic range. Transposing to a common key removes key-related variations. Normalizing tempo ensures consistent timing information. Standardizing instrumentation helps the model learn consistent relationships between notes and instruments. Converting to MP3 loses the symbolic representation of music notes which is vital for this application, so E is incorrect.

NEW QUESTION # 175
You are building a multimodal generative A1 model that creates realistic indoor scenes by combining textual descriptions, floor plans (geospatial data), and object libraries. The goal is to generate high-quality 3D models of the scenes. However, the model often produces scenes with physically implausible object arrangements (e.g., objects floating in the air, overlapping furniture). How can you MOST effectively integrate physical constraints into the generation process to ensure more realistic scene compositions?

A. Train a separate discriminator network that evaluates the physical plausibility of generated scenes and penalizes implausible configurations during training.
B. Force the model to generate only scenes that exist within the training set.
C. Use a physics engine (e.g., NVIDIA PhysX) as a post-processing step to simulate the generated scene and correct any physically implausible object placements.
D. Implement a rule-based system that enforces basic physical constraints (e.g., objects must be supported by a surface, no object interpenetration) during the generation process.
E. Increase the size of the training dataset with more examples of realistic indoor scenes.

Answer: A,C,D

Explanation:
Using a physics engine for post-processing (B) directly simulates physical interactions. Implementing a rule-based system (C) enforces basic constraints. Training a discriminator (D) adds a learning component for physical plausibility. Increasing the dataset size (A) might help but doesn't guarantee physical plausibility. Limiting generation to the training set (E) restricts creativity and generalization.

NEW QUESTION # 176
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