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NVIDIA Generative AI Multimodal Sample Questions (Q168-Q173):

NEW QUESTION # 168
You are building a multimodal Generative A1 system to generate image captions based on both the visual content of an image and a short audio description of the scene. Which architectural approach would be MOST effective for fusing these two modalities into a coherent representation for caption generation?

A. Early Fusion: Concatenate the raw image pixel data with the raw audio waveform data before feeding it into a single model.
B. Intermediate Fusion: Train separate image and audio encoders, then use cross-attention mechanisms to allow the image features to attend to the audio features (and vice-versa) at multiple layers of the model.
C. Ignore the audio entirely, as images are sufficient for generating captions.
D. Concatenate the image file name with the audio file name before feeding into the LLM.
E. Late Fusion: Train separate image and audio encoders, then concatenate their high-level feature vectors before feeding into a caption generation model.

Answer: B

Explanation:
Intermediate Fusion, particularly using cross-attention, allows for nuanced interaction between the modalities at multiple levels of abstraction. Early fusion is generally ineffective due to the vast differences in data type. Late fusion may miss important correlations. Ignoring a modality is obviously suboptimal when aiming for multimodal understanding.

NEW QUESTION # 169
You are building a multimodal model that combines text and image data to generate captions. The text encoder is a pre-trained BERT model, and the image encoder is a ResNet-50. You observe that the generated captions are heavily biased towards descriptions based on the text input, and the image information is not well represented. Which of the following techniques could you apply to improve the contribution of the image modality?

A. Freeze the weights of the ResNet-50 image encoder
B. Decrease the dimensionality of the image embeddings using PCA
C. Increase the learning rate of the BERT text encoder
D. Increase batch size substantially.
E. Apply a modality-specific loss weight, giving higher weight to the image loss during training-

Answer: E

Explanation:
Applying a modality-specific loss weight allows you to explicitly control the importance of each modality during training. By increasing the weight of the image loss, you encourage the model to pay more attention to the image information and generate captions that are more representative of the visual content. Increasing BERT's learning rate could worsen the imbalance. PCA is a data reduction technique not a balancing technique, freezing the weights on resnet 50, will not allow the network to learn about the relationship, batch size is to do with training speed not modality balance.

NEW QUESTION # 170
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. Normalizing the tempo of all MIDl files to a standard BPM.
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. Standardizing the instrumentation by mapping different instrument patches to a predefined set.

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 # 171
You are analyzing a dataset of customer reviews for a new product using Natural Language Processing (NLP). The dataset contains both positive and negative reviews, but a significant portion of the negative reviews uses sarcasm. Which of the following NLP techniques would be MOST effective in accurately identifying the sentiment expressed in sarcastic reviews?

A. Fine-tuning a pre-trained transformer model (e.g., BERT, RoBERTa) on a dataset of sarcastic and non-sarcastic reviews.
B. Calculating the average word embedding for each review.
C. Using a rule-based system that identifies keywords associated with positive or negative sentiment.
D. Bag-of-words model with TF-IDF weighting.
E. Sentiment lexicon-based approach.

Answer: A

Explanation:
Pre-trained transformer models capture contextual information and complex relationships between words, which is crucial for detecting sarcasm. Bag-of-words, sentiment lexicons, and rule-based systems rely on individual words or simple patterns and are easily fooled by sarcasm. Averaging word embeddings loses the contextual information vital for sarcasm detection.

NEW QUESTION # 172
Consider the following scenario: You are building a multimodal system for autonomous driving that uses both camera images and LiDAR data to perceive the environment. The LiDAR data is sparse and noisy, while the camera images are rich in visual details but can be affected by lighting conditions. Which of the following fusion strategies is MOST robust and effective for combining these two modalities?

A. Rely solely on the LiDAR data and discard the camera images to avoid lighting problems.
B. Rely solely on the camera images and discard the LiDAR data to simplify the system.
C. Train separate models for each modality and combine their predictions at the end using a simple averaging scheme.
D. Fuse the raw sensor data directly by concatenating the LiDAR point cloud and the image pixels.
E. Use an early fusion approach where the features extracted from each modality are combined at an early stage of the processing pipeline, allowing the model to learn cross-modal interactions.

Answer: E

Explanation:
Early fusion allows the model to learn complex relationships between the two modalities, leveraging the strengths of each modality while mitigating their weaknesses. Raw data fusion can be difficult to train. Late fusion might miss important cross-modal interactions. Discarding either modality reduces the information available to the system. Camera images are important even though they might be affected by lighting conditions.

NEW QUESTION # 173
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2025 NCA-GENM Exam Labs | High-quality 100% Free Valid Braindumps NCA-GENM Files

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NVIDIA Generative AI Multimodal Sample Questions (Q168-Q173):

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