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Examinations NCA-GENM Actual Questions | NCA-GENM Reliable Exam Practice

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NVIDIA Generative AI Multimodal Sample Questions (Q384-Q389):

NEW QUESTION # 384
You are working with a pre-trained multimodal model that takes images and text as input. You want to fine-tune this model for a specific downstream task, but you have limited computational resources. Which of the following techniques would be most effective for reducing the memory footprint and computational cost during fine-tuning?

A. Applying knowledge distillation, where a smaller student model is trained to mimic the behavior of the pre-trained model.
B. Freezing all layers of the pre-trained model and training only a small classification head.
C. Fine-tuning the entire model with a small learning rate.
D. Using quantization to reduce the precision of the model's weights and activations.
E. Increasing the batch size to utilize the available memory more efficiently.

Answer: A,D

Explanation:
Quantization reduces the memory footprint of the model by using lower-precision representations for weights and activations. Knowledge distillation allows you to train a smaller, more efficient model that performs similarly to the larger pre-trained model. Freezing layers reduces the number of trainable parameters but may limit the model's ability to adapt to the new task. Fine-tuning the entire model, even with a small learning rate, is computationally expensive. Increasing batch size might lead to Out of Memory errors.

NEW QUESTION # 385
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 prevent vanishing gradients during training of deep neural networks.
C. Attention mechanisms are primarily used to reduce the computational cost of processing long sequences.
D. Attention mechanisms allow the model to focus on the most relevant parts of the input sequence when generating the output.
E. Attention mechanisms enable the model to learn relationships between different modalities, such as images and text.

Answer: D,E

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 # 386
Consider the following code snippet using NVIDIA Triton Inference Server. What is the purpose of the 'sequence_batching' configuration?

A. It automatically scales the number of model instances based on the input load.
B. It enables dynamic batching based on request arrival times.
C. It allows for processing sequences of inputs (e.g., time series data) by maintaining state between requests.
D. It enables batching of independent requests to improve throughput.
E. It optimizes the model for specific hardware architectures.

Answer: C

Explanation:
The 'sequence_batching' configuration in Triton Inference Server is designed to handle sequential data where the server needs to maintain state between requests. This is essential for tasks like time-series prediction or conversational AI where the context of previous inputs matters. A, E describe standard batching. C describes autoscaling and D describes model optimization.

NEW QUESTION # 387
You are building a system that translates sign language videos into spoken text. You have a dataset of videos and corresponding text transcriptions. You notice that the test data contains significant variations in lighting conditions and camera angles compared to the training dat a. Which of the following techniques would be MOST effective in addressing this domain shift and improving the generalization of your model?

A. Use a domain adaptation technique such as Domain Adversarial Neural Networks (DANN) to learn domain-invariant features.
B. Fine-tune the model on a small subset of the test data to adapt to the specific characteristics of the test distribution.
C. Apply aggressive data augmentation techniques to the training data, including random crops, rotations, and color jittering to simulate the variations in the test data.
D. Reduce the size of the model to prevent overfitting to the training data.
E. Only evaluate on a subset of the test data that closely resembles the training data.

Answer: A

Explanation:
Domain adaptation techniques (C) are specifically designed to address domain shift by learning features that are invariant to the source and target domains. Data augmentation (A) can help but might not be sufficient. Fine-tuning on test data (B) is data leakage and invalidates the test set. Reducing model size (D) may not address the core issue of domain shift. Selecting a subset of the test data (E) defeats the purpose of testing generalization.

NEW QUESTION # 388
You are training a multimodal generative A1 model for image captioning. After initial training, you observe that the model excels at describing common objects but struggles with nuanced details and rare objects. Which of the following performance optimization strategies would be MOST effective in addressing this issue?

A. Apply early stopping to prevent overfitting to the common objects.
B. Increase the batch size during training to improve GPU utilization.
C. Reduce the learning rate to fine-tune the model on the existing dataset.
D. Implement a custom loss function that penalizes inaccuracies in describing rare objects more heavily.
E. Increase the number of layers in the encoder network.

Answer: D

Explanation:
Implementing a custom loss function is the most effective strategy because it directly addresses the model's weakness by focusing on accurate descriptions of rare objects. Increasing batch size improves training speed but not necessarily accuracy. Early stopping prevents overfitting, but doesn't specifically target the issue of rare object recognition. Reducing the learning rate might help with fine-tuning, but not as effectively as a targeted loss function. Increasing the number of layers may increase complexity but not guarantee better performance on rare objects.

NEW QUESTION # 389
......

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