Exam Topics NVIDIA NCA-GENM Pdf, NCA-GENM Interactive Questions

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LatestCram has designed NVIDIA Generative AI Multimodal which has actual exam Dumps questions, especially for the students who are willing to pass the NVIDIA NCA-GENM exam for the betterment of their future. The study material is available in three different formats. NVIDIA Practice Exam are also available so the students can test their preparation with unlimited tries and pass NVIDIA Generative AI Multimodal (NCA-GENM) certification exam on the first try.

NVIDIA Generative AI Multimodal Sample Questions (Q308-Q313):

NEW QUESTION # 308
You are developing a system that uses multimodal data (images, audio, and text) to detect fraudulent insurance claims. The image data represents damage to vehicles, the audio data captures conversations between the claimant and the insurance agent, and the text data includes the claim form details. What are the potential benefits of using multimodal data compared to relying on a single modality?

A. Simplified data preprocessing and feature engineering.
B. Increased vulnerability to adversarial attacks and data noise.
C. Reduced computational complexity and training time compared to using a single modality.
D. Improved accuracy and robustness due to complementary information from different modalities.
E. Ability to handle missing or incomplete data in one modality by relying on information from other modalities.

Answer: D,E

Explanation:
Multimodal data offers improved accuracy and robustness because different modalities provide complementary information that can compensate for weaknesses in individual modalities. It also allows for handling missing data by leveraging information from other modalities. However, multimodal systems typically have higher computational complexity and may require more sophisticated data preprocessing and feature engineering. While they can be more robust, the complexity can also potentially introduce new vulnerabilities, especially to adversarial attacks.

NEW QUESTION # 309
Consider a multimodal emotion recognition system that uses both facial expressions (images) and speech (audio). You want to fuse the information from these two modalities at the decision level. Which of the following techniques would be MOST suitable for decision-level fusion?

A. Train separate classifiers for images and audio, then use the output of the image classifier as input to the audio classifier-
B. Train a single transformer to process both images and audio in sequence.
C. Train separate classifiers for images and audio, then average their output probabilities for each emotion class.
D. Concatenate the feature vectors extracted from the images and audio, then train a single classifier.
E. Train separate classifiers for images and audio, then use a weighted average of their output probabilities based on the confidence scores of each classifier.

Answer: E

Explanation:
Weighted averaging allows you to give more weight to the modality that is more reliable or confident in its prediction for a given input. Simply averaging treats all modalities equally. Concatenation is feature-level fusion. The image classifier as input to audio classifier is a specific cascade approach. Using a single transformer is possible, but less common for decision fusion specifically. It is feature level fusion.

NEW QUESTION # 310
You're training a multimodal model for image and text retrieval. Given an image, the model should retrieve the most relevant text description from a database, and vice-vers a. You're using a dual-encoder architecture, where one encoder processes images and the other processes text, projecting them into a shared embedding space. What is the most effective way to train the model to ensure that semantically similar images and texts have close embeddings, while dissimilar ones have distant embeddings?

A. Train the encoders independently using separate supervised tasks for image and text classification.
B. Use a reconstruction loss that forces the model to reconstruct the input image from its text embedding and vice-versa.
C. Use a simple L1 loss between the image and text embeddings-
D. Apply adversarial training to make the embeddings indistinguishable between the two modalities.
E. Use a contrastive loss function that minimizes the distance between embeddings of matching image-text pairs and maximizes the distance between embeddings of non-matching pairs. Example: Triplet Loss, InfoNCE.

Answer: E

Explanation:
Contrastive loss functions are specifically designed for learning embeddings where similarity is defined by distance. They directly encourage similar items to be close and dissimilar items to be far apart. Independent training doesn't enforce the multimodal relationship. Reconstruction loss focuses on regenerating the input, not similarity. Adversarial training aims for indistinguishability, not meaningful embeddings. L1 Loss is a basic distance metric but less effective than contrastive losses for learning semantic similarity

NEW QUESTION # 311
Which of the following statements accurately describes the role of attention mechanisms in Transformer-based multimodal models?
(Select all that apply)

A. Attention mechanisms are used to compress the input sequence into a fixed-length vector representation.
B. Attention mechanisms allow the model to focus on the most relevant parts of the input sequence when generating the output.
C. Attention mechanisms enable the model to learn relationships between different modalities, such as images and text.
D. Attention mechanisms prevent vanishing gradients during training of deep neural networks.
E. Attention mechanisms are primarily used to reduce the computational cost of processing long sequences.

Answer: B,C

Explanation:
Attention mechanisms enable the model to selectively focus on relevant parts of the input and learn relationships between modalities. They don't compress the input into a fixed-length vector, nor are they primarily for reducing computational cost or preventing vanishing gradients (although they can indirectly help with the latter).

NEW QUESTION # 312
Which of the following techniques are MOST likely to improve the energy efficiency of a large-scale multimodal AI model without significantly sacrificing accuracy?

A. Model quantization (e.g., converting weights from FP32 to INT8).
B. Knowledge distillation to train a smaller student model.
C. Using a larger, more complex model architecture.
D. Increasing the batch size during training.
E. Applying pruning techniques to remove less important connections in the model.

Answer: A,B,E

Explanation:
Model quantization reduces the memory footprint and computational requirements by using lower precision numbers. Knowledge distillation transfers knowledge from a large model to a smaller one, reducing the computational cost. Pruning removes redundant connections, making the model more efficient. Increasing batch size (Option B) can improve throughput but doesn't inherently reduce energy consumption per sample. Using a larger model (Option D) increases energy consumption.

NEW QUESTION # 313
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There are three versions of our NCA-GENM study questions on our website: the PDF, Software and APP online.

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Exam Topics NVIDIA NCA-GENM Pdf, NCA-GENM Interactive Questions

NCA-GENM Practice Materials & NCA-GENM Test Torrent & NCA-GENM Pass King

NVIDIA Generative AI Multimodal Sample Questions (Q308-Q313):

Exam Topics NCA-GENM Pdf | Amazing Pass Rate For NVIDIA NCA-GENM | NCA-GENM: NVIDIA Generative AI Multimodal

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