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NVIDIA Generative AI Multimodal Sample Questions (Q217-Q222):

NEW QUESTION # 217
When training a multimodal model with both text and image data, what is a common challenge related to the different characteristics and scales of these modalities, and what are some common strategies to address it? (Select TWO correct answers)

A. Modalities often have different scales and distributions, leading to one modality dominating the learning process.
B. Using modality-specific normalization techniques and carefully weighting the loss contributions from each modality.
C. Images are always processed faster than text, requiring artificial delays in the text processing pipeline.
D. Text data inherently contains more information than image data, making it difficult to balance their contributions.
E. Always training the image processing part first and freezing the weights before text processing

Answer: A,B

Explanation:
Different modalities often have different scales and distributions, leading to one modality dominating the learning process. To address this, modality-specific normalization techniques (e.g., batch normalization for images and layer normalization for text) and carefully weighting the loss contributions from each modality are often used.

NEW QUESTION # 218
A self-driving car uses multimodal data (camera images, LiDAR point clouds, radar data, and GPS information) to navigate. The LiDAR sensor occasionally fails, resulting in missing point cloud dat a. How should the system be designed to handle this sensor failure gracefully and maintain safe navigation?

A. Rely solely on the camera images and ignore the missing LiDAR data.
B. Use a sensor fusion technique that prioritizes the available modalities (camera, radar, GPS) and estimates the missing LiDAR data based on these modalities.
C. Switch to a pre-programmed route that does not require LiDAR data.
D. Employ a Kalman filter to predict the LiDAR point cloud based on the previous sensor readings and the car's motion model.
E. Immediately stop the car until the LiDAR sensor is fixed.

Answer: B,D

Explanation:
Stopping the car or relying solely on a single modality is not a robust solution. Using sensor fusion to prioritize available modalities and estimate missing data allows the system to continue navigating safely. A Kalman filter is a specific technique for estimating the state of a system (in this case, the LiDAR point cloud) based on noisy sensor readings and a motion model.

NEW QUESTION # 219
A financial institution is developing a multimodal A1 system to detect fraudulent transactions by analyzing transaction details (text), user images, and audio recordings of phone calls. Which of the following strategies is MOST crucial for handling the missing data that frequently occurs across these modalities?

A. Using a modality dropout technique during training, randomly masking modalities to force the model to learn robust representations from incomplete data.
B. Employing a joint imputation approach that leverages information from available modalities to predict and fill in missing values in other modalities.
C. Ignoring transactions with missing data to simplify the model's training process.
D. Imputing missing data in each modality independently using modality-specific imputation techniques (e.g., mean imputation for numerical data, most frequent category for categorical data).
E. Replacing missing data with a single, arbitrary placeholder value (e.g., -1 for numerical data, 'missing' for text) across all modalities.

Answer: A,B

Explanation:
Ignoring missing data or using simple imputation techniques can introduce bias and reduce the model's accuracy. A joint imputation approach is superior because it leverages the relationships between modalities to improve imputation accuracy. Modality dropout during training further enhances robustness to missing data in real-world scenarios.

NEW QUESTION # 220
You are experimenting with different architectures for a text-to-speech (TTS) model. You have implemented a Tacotron 2 model and a FastSpeech 2 model. Which of the following statements accurately describes the key differences between these two architectures and their implications?

A. Tacotron 2 uses an attention mechanism for aligning text and speech, while FastSpeech 2 relies on a fixed alignment, resulting in faster training and inference for FastSpeech 2 but potentially lower quality.
B. Both Tacotron 2 and FastSpeech 2 use attention mechanisms, but FastSpeech 2 incorporates length regulator and variance adaptor modules to address the one-to-many mapping problem, leading to more stable and controllable synthesis.
C. Tacotron 2 is an autoregressive model, while FastSpeech 2 is a non-autoregressive model. This allows FastSpeech 2 to generate speech in parallel, resulting in significantly faster inference speeds.
D. Both Tacotron 2 and FastSpeech 2 rely on fixed alignments, but FastSpeech 2 uses a more complex decoder architecture, leading to higher quality but slower inference.
E. FastSpeech 2 uses an attention mechanism for aligning text and speech, while Tacotron 2 relies on a fixed alignment, resulting in faster training and inference for Tacotron 2 but potentially lower quality.

Answer: B,C

Explanation:
Tacotron 2 is an autoregressive model that uses an attention mechanism for aligning text and speech, whereas FastSpeech 2 is a non-autoregressive model, generating speech in parallel for faster inference. FastSpeech 2 also addresses the one-to-many mapping problem (one phoneme can have different durations) with length regulator and variance adaptor modules, improving stability and controllability. A is incorrect because attention based Tacotron 2 is slow to train and infer. B and D are incorrect becuase Tacotron 2 architecture has attention mechanism.

NEW QUESTION # 221
You're developing a multimodal A1 system that takes image data, text descriptions, and user interaction data (clicks, dwell time) to generate personalized product recommendations. To effectively combine these modalities and capture complex relationships, which model architecture would be most suitable?

A. A deep learning architecture incorporating attention mechanisms and cross-modal fusion layers, with separate embedding layers for each modality, followed by a shared representation layer for joint learning and prediction.
B. A simple linear regression model.
C. A k-nearest neighbors (KNN) algorithm.
D. A Naive Bayes classifier.
E. A decision tree-based model.

Answer: A

Explanation:
Deep learning architectures with attention mechanisms and cross-modal fusion layers are best suited for capturing complex relationships between different modalities. Attention mechanisms allow the model to focus on the most relevant features from each modality, while cross-modal fusion layers enable joint learning and prediction based on the combined representations. Linear regression, decision trees, KNN, and Naive Bayes are less capable of capturing complex, non-linear relationships in multimodal data.

NEW QUESTION # 222
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New NCA-GENM Test Pdf | Valid NCA-GENM Test Questions

Valid NCA-GENM Test Questions & Latest NCA-GENM Test Prep

NVIDIA Generative AI Multimodal Sample Questions (Q217-Q222):

Marvelous NVIDIA NCA-GENM: New NVIDIA Generative AI Multimodal Test Pdf - 100% Pass-Rate Lead1Pass Valid NCA-GENM Test Questions

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